I built this partly because i wanted a cleaner side-by-side comparison of the big “2026 outlook” reports (banks + asset managers), and partly as an experiment in whether codex / claude code / cursor together can be pushed to handle citations accurately rather than hallucinating summaries. Quite pleased with the result.
The workflow was roughly: ingest publicly available outlook reports, extract claims, numbers, and framing (& force every synthesized statement to be tied back to a specific source/page), anchor on key themes (ai capex, policy path, power/energy, credit, etc.) rather than by institution.
You're still captive to a product. Which means that when CloudCo. increases their monthly GenAI price from $50/mo. to $500/mo., you're losing your service or you're paying. By participating in the build process you're giving yourself a fighting chance.
I will quickly forget the details about any given code base within a few months anyway. Having used AI to build a project at least leaves me with very concise and actionable documentation and, as the prompter, I will have a deep understanding of the high-level vision, requirements and functionality.
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.
Author here, really good comment and I agree with you.
What _has_ surprised me though is just how many companies are (or are considering) building 'internal' tooling to replace SaaS they are not happy with. These are not the classic HN types whatsoever. I think when non technical people get to play with AI software dev they go 'wow so why can't we do everything like this'.
I think your point 3 is really interesting too.
But yes the point of my article (hopefully) wasn't that SaaS is overnight dead, but some thin/lower "quality" products are potentially in real trouble.
People will still buy and use expertly designed products that are really nice to use. But a lot of b2b SaaS is not that, its a slow clunky mess that wants to make you scream!
I agree - it is surprising how many are looking at doing in house.
I think what they miss (and I say this as someone who spent the early part of his career outside of tech) is an understanding of what goes into maintaining software products - and this ignorance will be short lived. I was honestly shocked how complex it was to build and maintain my first web app. So business types (like I was) who are used to 'maintaining' an excel spreadsheet and powerpoint deck they update every quarter may think of SaaS like a software license they can build once and use forever. They have no appreciation of the depth of challenges that come with maintaining anything in production.
My working model is that of no-code - many non-tech types experimented with bubble etc, but quickly realize that tech products are far deeper than the (heavily curated) surface level experience that the user has. It is not like an excel model where the UI is codebase. I expect vibe-coders will find the same thing.
I have on several occasions built my own versions of tools, only to cave and buy a $99 a year off the shelf version because the maintenance time isn't worth it. Non-tech folks have no idea of the depth of pain of maintaining any system.
They will learn. Will be interesting to see how it plays out.
I like this thoughtful and nuanced response, I think you could be right. Makes me wonder if choosing an extremely boring niche and just making several million dollars could be a good move right now.
Quite honestly, this is exactly what I am currently doing - identified a market with probably $50mm global TAM. Bootstrapping with first design partners currently.
One thing I didn't mention is that there are often a few sleepy legacy SaaS players (often public) in these niche markets who don't have the chops to add AI to their product and may be a good takeout / exit down the line. Won't be for billions, but if you bootstrap, that doesn't really matter.
Strava is the closest thing to effective social media that I use, because it is 'slow'.
You create 'content' by doing something orthogonal.
You pay for access vs selling attention for ads.
You only look at Strava when you are working out, so engagement is authentic vs contrived/performative. I care about my friend completing a run because I know they did it.
One of the best thing they did was allow the chance to add photos.
This may have been a contributing factor, but the reason the American frigates were successful in the War of 1812 was because they were significantly larger and more powerful ships, with more powerful cannons. They had material advantages in size (100% heavier), crew (50% more numerous), and firepower (30-50% more weight of shot). The crews were well trained (and included many former British navy able seamen, pressed from American merchantmen) but it was the material advantages that swayed these combats.
It was curious how effectively the American naval establishment gamed the European 'honor' system of naval warfare - they knew that if they kept these warships technically rated as 'frigates' (even though they were the largest and most powerful frigates ever built, similar in size to smaller ships of the line), the British would still try to fight them one on one with their frigates.
Yes, it was old growth southern live oak, which is harder and denser than the oak the British used in their warships. Hence the Constitution's apt nickname of "Old Ironsides".
It is a talent and a distribution play.
Talent: obvious.
Distribution: OpenAI believes the marginal token they sell will be accretive to their bottom line, so the goal then is to deliver as many tokens as possible. Windsurf already has 1k+ enterprise logos and allegedly millions of downloads. 2 m tokens × $0.00001 gross / token = $20/seat/mo; if windsrf runs 500k seats, oai books $120 m/yr gross @ 90% margin.
I saw a similar dynamic play out in the UK with Pub (bar) companies. By mid-2000s, the major players were failing. Margins were nearly zero, thanks to rising costs, and securlar decline in demand, plus they had too much expensive term debt.
But they represented profitable sources of distribution for the beer makers. So Heineken went on a buying spree. They didn't care about making money from the pubs themselves and were happy to run them break-even. This is because they then had a controlled channel of distribution for their beer (and they made a profit on every pint they shipped).
The switching costs are very different here, and the market is still so nascent. It is a thin product and vscode‑copilot can catchup. But 1% of enterprise value ($3bn of $300bn) is not a lot to gamble on owning the #2 horse in the most promising AI end market today.
I mention distribution in the post, and reasons to be skeptical of that as the primary driver (though I agree that may be the case)
Re talent, I'm not sure how big windsurf is, but aren't these teams generally quite small? $3b for a small team still seems quite high, especially since (afaik) their core area of expertise is more in UX and product than in ml research. That's not to say that UX and product aren't worth acquiring, just that the price tag is surprising if that's the primary justification.
Given Sam recently said he thinks consumer is going to be the valuable path, perhaps it is not too much to pay for a great ux/product team
"Ben Thompson: What’s going to be more valuable in five years? A 1-billion daily active user destination site that doesn’t have to do customer acquisition, or the state-of-the-art model?
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