5. Initially it did equally bad job but I noticed it had a switch "Real-time diff" which did exactly what I wanted.
6. I got curious what is this algorithm. So I asked Gemini with "Deep Research" mode: "The website https://www.diffchecker.com/ uses a diff algorithm they call real-time diff. It works really good for reformatted and corrected text documents. I'd like to know what is this algorithm and if there's any other software, preferably open-source that uses it."
7. As a first suggestion it listed diff-match-patch from Google. It had Python package.
8. I started Antigravity in a new folder, ran uv init. Then I prompted the following:
"I installed the missing dependance for you. Please continue." - I noticed it doesn't use uv for installing dependencies so I interrupted and did it myself.
[...]
"This project uses uv. To run python code use
uv run python test_diff.py" - I noticed it still doesn't use uv for running the code so its testing fails.
[...]
"Semantic cleanup is important, please use it." - Things started to show up but it looked like linear diff. I noticed it had a call to semantic cleanup method commented out so I thought it might help if I push it in that direction.
[...]
"also display the complete, raw diff object below the table" - the display of the diff still didn't seem good so I got curious if it's the problem with the diffing code or the display code
[...]
"I don't see the contents of the object, just text {diffs}" - it made a silly mistake by outputting template variable instead of actual object.
[...]
"While comparing larger files 1.txt and 2.txt I notice that the diff is not very granular. Text changed just slightly but the diff looks like deleting nearly all the lines of the document, and inserting completely fresh ones. Can you force diff library to be more granular?
Maybe there's some better matching algoritm in the library?" - it seemed that while on small tests that Antigravity made itself it worked decently but on the texts that I actually wanted to compare was still terrible although I've seen glimpses of hope because some spots were diffed more granularly. I inspected the code and it seemed to be doing character level diffing as per diff-match-patch example. While it processed this prompt I was searching for solution myself by clicking around diff-match-patch repo and demos. I found a potential solution by adjusting cleanup, but it actually solved the problem by itself by ditching the character level diffing (which I'm not sure I would have come up with at this point). Diffed object looked great but as I co...
Another post with no data, but plenty of personal vibes.
> The signals I'm seeing
Here are the signals:
> If I want an internal dashboard...
> If I need to re-encode videos...
> This is even more pronounced for less pure software development tasks. For example, I've had Gemini 3 produce really high quality UI/UX mockups and wireframes
> people really questioning renewal quotes from larger "enterprise" SaaS companies
Maybe someday we'll see job postings for maintaining these in-house SaaS tools. And someday someday, we'll see these in-house SaaS tools being consolidated as its own separate product. Wait what.
Summary is that for agents to work well they need clear vision into all things, and putting the data behind a gui or not well maintained CLI is a hinderance. Combined with how structured crud apps are an how the agents can for sure write good crud apps, no reason to not have your own. Wins all around with not paying for it, having a better understanding of processes, and letting agents handle workflows.
This is why I started working on an open source, generic protobuf sqlite ORM + CRUD server (with search/filtering) + type/service registry + grpc mesh, recently: https://github.com/accretional/collector Note: collector's docs are mostly from LLMs, partially because it's more of a framework for tool-calling LLMs than humans
Then this project lets you generate static sites from svelte components (matches protobuf structures) and markdown (documentation) and global template variables: https://github.com/accretional/statue
A lot of the SaaS ecosystem actually has rather simple domain logic and oftentimes doesn't even model data very well, or at least not in a way that matches their clients/users mental models or application logic. A lot of the value is in integrations, or the data/scaling, or the marketing and developer experience, or some kind of expertise in actually properly providing a simple interface to a complex solution.
So why not just create a compact universal representation of that? Because it's not so big a leap to go beyond eating SaaS to eating integrations, migration costs/bad moats, and the marketing/documentation/wrapper.
I often give the follow analogy which I think is a good proxy to what is going on.
Spreadsheets! They are everywhere. In fact, they are so abundant these days that that many are spawned for a quick job and immediately discarded. In fact, the cost of having these spreadsheets is practically zero so in many cases one may find themselves having hundreds if not thousands of them sitting around with no indication to ever being deleted. Spreadsheets are also personal and annoying especially when forced upon you (since you did not make it yourself). Spreadsheets are also programming for non-programmers.
These new vibe-coded tools are essentially the new spreadsheets. They are useful,... for 5 minutes. They are also easily forgettable. They are also personal (for the person who made them) and hated (by everyone else). I have no doubt in my mind that organisation will start using more and more of these new types of software to automate repetitive tasks, improve existing processes and so on but ultimately, apart from perhaps just a few, none will replace existing, purpose-built systems.
Ultimately you can make your own pretty dashboard that nobody else will see or use because when the cost of production is so low your users will want to create their own version because they would think they could do better.
After all, how hard is to prompt harder then the previous person?
Also, do you really think that SaaS companies are not deploying AI themselves? It is practically an arms race: the non-expert plus some AI vs 10 specialist developers plus their AIs doing this all day long.
I mostly agree. What it does do is raise the bar for a viable SaaS, and seeing some examples elsewhere in this thread, that’s a good thing.
I’d also add a number of the vibe tools tech adjacent people on my team have made are used and liked by the team. Even engineering likes them because it frees up their time to work on customer facing things.
There are SO MANY Excel sheets someone automated a decade+ ago still used in essential parts of the government and big corporations.
Nobody knows how they work, very few have the skills or time to edit them or check them. People just use them for the convenience.
The magic sauce of Excel is that it's free an scriptable (programmable even). If you want a SaaS, you need to involve IT, Legal, your supervisors and it's a whole-ass thing of contracts and shit.
Excel? It's just there.
There are so many stories and anecdotes of people being in stupid data entry jobs, getting bored and finding out their whole job can be automated with a single smartly done Excel sheet. Then they press F9 once per day and do something else for the rest of the time =)
And just because, my main gripe about Excel: there are no unit tests or validators for it. There's no easy way to programmatically confirm that Cell C5 has the same formula as C875
If (when?) people start AI-coding the things they used to use Excel for, we might get some actual tests and validation to confirm what the code is supposed to actually happens.
I get and agree with a lot of skepticism (and I get where ad-hominem attacks come from:). I have AI shoved my throat at work and at home 24x7 and most of it not for my benefit, and the writer doesn't out as much rigor into writing as might be beneficial.
At the same time, to the core theme of the article - do any of us think a small sassy SaaS like Bingo card creator could take off now? :-)
There is a significant risk of uncertainty in all of this, the most damaging aspect really. If AI improves, and it is threatening to, then growth in SaaS may decline to a point where investing in it needs to be reconsidered.
The problem is, nobody knows how much and how fast AI will improve or how much it will cost if it does.
That uncertainty alone is very problematic and I think is being underestimated in terms of its impact on everything it can potentially touch.
For now though, I've seen a wall form in benchmarks like swe-rebench and swebench pro. Greenfield is expanding, but maintenance is still a problem.
I think AI needs to get much better at maintenance before serious companies can choose build over buy for anything but the most trivial apps.
Good point! For me the only change that happened so far (because the agentic product was better) was switching from JetBrains to Cursor. I'm sure this will happen with more products I use in the future
I’m currently working on an in house ERP and inventory system for a specific kind of business. With very few people you can now instead of paying loads of money for some off the shelf solution to your software needs get something completely bespoke to your business. I think AI enables the age of boutique software that works fantastically for businesses, agencies will need to dramatically reduce their price to compete with in house teams.
I’m pretty certain AI quadruples my output at least and facilitates fixing, improving and upgrading poor quality inherited software much better than in the past. Why pay for SaaS when you can build something “good enough” in a week or two? You also get exactly what you want rather than some £300k per year CRM that will double or treble in price and never quite be what you wanted.
To me AI might have tilted the economic on doing in house a bit but it has been at least a decade or more that I find most enterprise SaaS, in the way they are used 80% of the time, could be recreated with a few developers in house. Instead of 10-20 developers maybe you only need 2-5 with AI, so for most big companies that doesn’t change much. A company that wants to build in house still has to hire a team. And in most non tech industries even if more expensive usually a service is preferred. SaaS was never (only) about costs, developers were already wondering why people would pay for an expensive CRM 10 years ago when it was only basic CRUD.
I agree about boutique software, but see the development still being external -
To attempt to summarize the debate, there seems to be three prevailing schools of thought:
1. Status Quo + AI. SaaS companies will adopt AI and not lose share. Everyone keeps paying for the same SaaS plus a few bells and whistles. This seems unlikely given AI makes it dramatically cheaper to build and maintain SaaS. Incumbents will save on COGS, but have to cut their pricing (which is a hard sell to investors in the short term).
2. SaaS gets eaten by internal development (per OP). Unlikely in short/medium term (as most commenters highlight). See: complete cloud adoption will take 30+ years (shows that even obviously positive ROI development often does not happen). This view reminds me a bit of the (in)famous DropBox HN comment(1) - the average HN commenter is 100x more minded to hack and maintain their own tool than the market.
benzible (commenter) elsewhere said this well -
"The bottleneck is still knowing what to build, not building. A lot of the value in our product is in decisions users don't even know we made for them. Domain expertise + tight feedback loop with users can't be replicated by an internal developer in an afternoon."
This same logic explains why external boutique beats internal builds --
3. AI helps boutique-software flourish because it changes vendor economics (not buyer economics). Whereas previously an ERP for a specific niche industry (e.g. wealth managers who only work with Canadian / US cross-border clients) would have had to make do with a non-specific ERP, there will now be a custom solution for them. Before AI, the $20MM TAM for this product would have made it a non-starter for VC backed startups. But now, a two person team can build and maintain a product that previously took ten devs. Distribution becomes the bottleneck.
This trend has been ongoing for a while -- Toast, Procore, Veeva -- AI just accelerates it.
If I had to guess, I expect some combination of all three - some incumbents will adapt well, cut pricing, and expand their offering. Some customers will move development in house (e.g. I have already seen several large private equity firms creating their own internal AI tooling teams rather than pay for expensive external vendors). And there will be a major flourishing of boutique tools.
Ah, yes. If the thing that is false is true, all kinds of interesting things happen! For example, if I became the queen of France, I could make people do silly dances! That is an interesting hypothesis that could play out in my imaginary world!
SaaS maintenance isn't about upgrading packages, it's about accountability and a point of contact when something breaks along with SLAs and contractual obligations. It isn't because building a kanban board app is hard. Someone else deals with provisioning, alerts, compliance, etc. and they are a real human who cannot hallucinate that the issue has been fixed when it hasn't. Depending on the contract and how it is breached, you can potentially take them to court and sue them to recover money lost as a result of their malpractice. None of that applies to a neural network that misreads the alert, does something completely wrong, then concludes the issue is fixed the way the latest models constantly do when I use them.
The real question isn’t whether we’ll run out of SaaS customers, it’s whether we’ll run out of new problems that can be solved by the current set of tools. I doubt it, it’d be a historical first in the modern era. But the solutions may move closer to the companies with the problems. More in-house, fewer intermediaries.
"It was always possible to clone software, but doing so was costly and time consuming, and the clone would need to be much cheaper, making any such venture financially non-viable.
With AI, that equation is now changing. I anticipate that within 5 years autonomous coding agents will be able to rapidly and cheaply clone almost any existing software, while also providing hosting, operations, and support, all for a small fraction of the cost.
This will inevitably destroy many existing businesses. In order to survive, businesses will require strong network effects (e.g. marketplaces) or extremely deep data/compute moats. There will also be many new opportunities created by the very low cost of software. What could you build if it were possible to create software 1000x faster and cheaper?"
This article made no sense to me. It is talking about AI-generated code eating SaaS. That's not what is going to replace SaaS. When AI is able to do the job itself — without generating code — that's what is going to replace SaaS.
AI-generated code still requires software engineers to build, test, debug, deploy, secure, monitor, be on-call, handle incidents, and so on. That's very expensive. It is much cheaper to pay a small monthly fee to a SaaS company.
Analytics like what?! Sentry? See, i'm the first one to waste 15+ hours of my own time claude vibing some barely working analytics in order to save 15 dollars for not paying a proven solution to professionals who really understand that problem domain - but we all agree how dumb this is. But if i really can vibe code that analytics in 5 minutes, that thing was never a proven SaaS business in the first place and my use case with certainty a toy app with zero users..
Note that there is zero actual sales/renewal data quoted in the article so this is all the authors vibes based on how he has been able to vibe code a few things for a team of one person to use
The bit about building an internal app for eg marketing or sales is super fun. Getting calls starting at 8am EST because they then (reasonably!) expect it to work less so. Software still has an enormous ktlo tax and until that changes, I'm skeptical about the entire thesis.
Not to mention the author appears to run a 1-2 person company, so ... yeah. AI thought leadership ahoy.
103 comments
[ 1.8 ms ] story [ 81.5 ms ] threadOh, child.... building is easy. Coordinating maintenance of the tool across a non-technical team is hell.
Also maintaining a software is pain
Also for perpetually small companies, its now easy to build simple scripts to be achieve some productivity gains.
1. I had two text documents containing plain text to compare. One with minor edits (done by AI).
2. I wanted to see what AI changed in my text.
3. I tried the usual diff tools. They diffed line by line and result was terrible. I searched google for "text comparison tool but not line-based"
4. As second search result it found me https://www.diffchecker.com/ (It's a SaaS, right?)
5. Initially it did equally bad job but I noticed it had a switch "Real-time diff" which did exactly what I wanted.
6. I got curious what is this algorithm. So I asked Gemini with "Deep Research" mode: "The website https://www.diffchecker.com/ uses a diff algorithm they call real-time diff. It works really good for reformatted and corrected text documents. I'd like to know what is this algorithm and if there's any other software, preferably open-source that uses it."
7. As a first suggestion it listed diff-match-patch from Google. It had Python package.
8. I started Antigravity in a new folder, ran uv init. Then I prompted the following:
"Write a commandline tool that uses https://github.com/google/diff-match-patch/wiki/Language:-Py... to generate diff of two files and presents it as side by side comparison in generated html file."
[...]
"I installed the missing dependance for you. Please continue." - I noticed it doesn't use uv for installing dependencies so I interrupted and did it myself.
[...]
"This project uses uv. To run python code use
uv run python test_diff.py" - I noticed it still doesn't use uv for running the code so its testing fails.
[...]
"Semantic cleanup is important, please use it." - Things started to show up but it looked like linear diff. I noticed it had a call to semantic cleanup method commented out so I thought it might help if I push it in that direction.
[...]
"also display the complete, raw diff object below the table" - the display of the diff still didn't seem good so I got curious if it's the problem with the diffing code or the display code
[...]
"I don't see the contents of the object, just text {diffs}" - it made a silly mistake by outputting template variable instead of actual object.
[...]
"While comparing larger files 1.txt and 2.txt I notice that the diff is not very granular. Text changed just slightly but the diff looks like deleting nearly all the lines of the document, and inserting completely fresh ones. Can you force diff library to be more granular?
You seem to be doing the right thing https://github.com/google/diff-match-patch/wiki/Line-or-Word... but the outcome is not good.
Maybe there's some better matching algoritm in the library?" - it seemed that while on small tests that Antigravity made itself it worked decently but on the texts that I actually wanted to compare was still terrible although I've seen glimpses of hope because some spots were diffed more granularly. I inspected the code and it seemed to be doing character level diffing as per diff-match-patch example. While it processed this prompt I was searching for solution myself by clicking around diff-match-patch repo and demos. I found a potential solution by adjusting cleanup, but it actually solved the problem by itself by ditching the character level diffing (which I'm not sure I would have come up with at this point). Diffed object looked great but as I co...
> The signals I'm seeing
Here are the signals:
> If I want an internal dashboard...
> If I need to re-encode videos...
> This is even more pronounced for less pure software development tasks. For example, I've had Gemini 3 produce really high quality UI/UX mockups and wireframes
> people really questioning renewal quotes from larger "enterprise" SaaS companies
Who are "people"?
Then it dawned on me how many companies are deeply integrating Copilot into their everyday workflows. It's the perfect Trojan Horse.
Summary is that for agents to work well they need clear vision into all things, and putting the data behind a gui or not well maintained CLI is a hinderance. Combined with how structured crud apps are an how the agents can for sure write good crud apps, no reason to not have your own. Wins all around with not paying for it, having a better understanding of processes, and letting agents handle workflows.
- anything that requires very high uptime
-very high volume systems and data lakes
-software with significant network effects
-companies that have proprietary datasets
-regulation and compliance is still very important
Then this project lets you generate static sites from svelte components (matches protobuf structures) and markdown (documentation) and global template variables: https://github.com/accretional/statue
A lot of the SaaS ecosystem actually has rather simple domain logic and oftentimes doesn't even model data very well, or at least not in a way that matches their clients/users mental models or application logic. A lot of the value is in integrations, or the data/scaling, or the marketing and developer experience, or some kind of expertise in actually properly providing a simple interface to a complex solution.
So why not just create a compact universal representation of that? Because it's not so big a leap to go beyond eating SaaS to eating integrations, migration costs/bad moats, and the marketing/documentation/wrapper.
Spreadsheets! They are everywhere. In fact, they are so abundant these days that that many are spawned for a quick job and immediately discarded. In fact, the cost of having these spreadsheets is practically zero so in many cases one may find themselves having hundreds if not thousands of them sitting around with no indication to ever being deleted. Spreadsheets are also personal and annoying especially when forced upon you (since you did not make it yourself). Spreadsheets are also programming for non-programmers.
These new vibe-coded tools are essentially the new spreadsheets. They are useful,... for 5 minutes. They are also easily forgettable. They are also personal (for the person who made them) and hated (by everyone else). I have no doubt in my mind that organisation will start using more and more of these new types of software to automate repetitive tasks, improve existing processes and so on but ultimately, apart from perhaps just a few, none will replace existing, purpose-built systems.
Ultimately you can make your own pretty dashboard that nobody else will see or use because when the cost of production is so low your users will want to create their own version because they would think they could do better.
After all, how hard is to prompt harder then the previous person?
Also, do you really think that SaaS companies are not deploying AI themselves? It is practically an arms race: the non-expert plus some AI vs 10 specialist developers plus their AIs doing this all day long.
Who is going to have the upper-hand?
I’d also add a number of the vibe tools tech adjacent people on my team have made are used and liked by the team. Even engineering likes them because it frees up their time to work on customer facing things.
Nobody knows how they work, very few have the skills or time to edit them or check them. People just use them for the convenience.
The magic sauce of Excel is that it's free an scriptable (programmable even). If you want a SaaS, you need to involve IT, Legal, your supervisors and it's a whole-ass thing of contracts and shit.
Excel? It's just there.
There are so many stories and anecdotes of people being in stupid data entry jobs, getting bored and finding out their whole job can be automated with a single smartly done Excel sheet. Then they press F9 once per day and do something else for the rest of the time =)
And just because, my main gripe about Excel: there are no unit tests or validators for it. There's no easy way to programmatically confirm that Cell C5 has the same formula as C875
If (when?) people start AI-coding the things they used to use Excel for, we might get some actual tests and validation to confirm what the code is supposed to actually happens.
At the same time, to the core theme of the article - do any of us think a small sassy SaaS like Bingo card creator could take off now? :-)
https://training.kalzumeus.com/newsletters/archive/selling_s...
The problem is, nobody knows how much and how fast AI will improve or how much it will cost if it does.
That uncertainty alone is very problematic and I think is being underestimated in terms of its impact on everything it can potentially touch.
For now though, I've seen a wall form in benchmarks like swe-rebench and swebench pro. Greenfield is expanding, but maintenance is still a problem.
I think AI needs to get much better at maintenance before serious companies can choose build over buy for anything but the most trivial apps.
The only named product was Retool.
I’m pretty certain AI quadruples my output at least and facilitates fixing, improving and upgrading poor quality inherited software much better than in the past. Why pay for SaaS when you can build something “good enough” in a week or two? You also get exactly what you want rather than some £300k per year CRM that will double or treble in price and never quite be what you wanted.
To attempt to summarize the debate, there seems to be three prevailing schools of thought:
1. Status Quo + AI. SaaS companies will adopt AI and not lose share. Everyone keeps paying for the same SaaS plus a few bells and whistles. This seems unlikely given AI makes it dramatically cheaper to build and maintain SaaS. Incumbents will save on COGS, but have to cut their pricing (which is a hard sell to investors in the short term).
2. SaaS gets eaten by internal development (per OP). Unlikely in short/medium term (as most commenters highlight). See: complete cloud adoption will take 30+ years (shows that even obviously positive ROI development often does not happen). This view reminds me a bit of the (in)famous DropBox HN comment(1) - the average HN commenter is 100x more minded to hack and maintain their own tool than the market.
benzible (commenter) elsewhere said this well - "The bottleneck is still knowing what to build, not building. A lot of the value in our product is in decisions users don't even know we made for them. Domain expertise + tight feedback loop with users can't be replicated by an internal developer in an afternoon."
This same logic explains why external boutique beats internal builds --
3. AI helps boutique-software flourish because it changes vendor economics (not buyer economics). Whereas previously an ERP for a specific niche industry (e.g. wealth managers who only work with Canadian / US cross-border clients) would have had to make do with a non-specific ERP, there will now be a custom solution for them. Before AI, the $20MM TAM for this product would have made it a non-starter for VC backed startups. But now, a two person team can build and maintain a product that previously took ten devs. Distribution becomes the bottleneck.
This trend has been ongoing for a while -- Toast, Procore, Veeva -- AI just accelerates it.
If I had to guess, I expect some combination of all three - some incumbents will adapt well, cut pricing, and expand their offering. Some customers will move development in house (e.g. I have already seen several large private equity firms creating their own internal AI tooling teams rather than pay for expensive external vendors). And there will be a major flourishing of boutique tools.
(1) https://news.ycombinator.com/item?id=9224
this means if I sell it to your business for the price of < your salary - you will get fired and business will use my version.
Why? because my will always be better as 10 people work on it vs you alone.
Internal versions will never be better or cheaper than saas (unless you are doing some tiny and very specific automation).
They can be better than current solution - but only a matter of time when someone makes a saas equal and better to what you do internally.
Sure almost anything will be better and cheaper that hubspot.
But with AI smaller CRMs that are hyper focused on businesses like yours will start popping up and eating its market.
Anything bigger than a toy project will always be cheaper/better to buy.
SaaS maintenance isn't about upgrading packages, it's about accountability and a point of contact when something breaks along with SLAs and contractual obligations. It isn't because building a kanban board app is hard. Someone else deals with provisioning, alerts, compliance, etc. and they are a real human who cannot hallucinate that the issue has been fixed when it hasn't. Depending on the contract and how it is breached, you can potentially take them to court and sue them to recover money lost as a result of their malpractice. None of that applies to a neural network that misreads the alert, does something completely wrong, then concludes the issue is fixed the way the latest models constantly do when I use them.
With AI, that equation is now changing. I anticipate that within 5 years autonomous coding agents will be able to rapidly and cheaply clone almost any existing software, while also providing hosting, operations, and support, all for a small fraction of the cost.
This will inevitably destroy many existing businesses. In order to survive, businesses will require strong network effects (e.g. marketplaces) or extremely deep data/compute moats. There will also be many new opportunities created by the very low cost of software. What could you build if it were possible to create software 1000x faster and cheaper?"
Paul Bucheit
https://x.com/paultoo/status/1999245292294803914
AI-generated code still requires software engineers to build, test, debug, deploy, secure, monitor, be on-call, handle incidents, and so on. That's very expensive. It is much cheaper to pay a small monthly fee to a SaaS company.
Are you as a dev still going to pay for analytics and dashboards that you could have propped up by Claude in 5 minutes instead?
Not to mention the author appears to run a 1-2 person company, so ... yeah. AI thought leadership ahoy.