Acharya’s framing is different from mine (he’s talking book on software stocks) but the conclusion is the same: the “innovation bazooka” pointed at rebuilding payroll is a bad allocation of resources. Benedict Evans called me out on LinkedIn for this (https://philippdubach.com/posts/is-ai-really-eating-the-worl...) take, which I take as a sign the argument is landing..
Both AI Fanatics and AI Luddites need to touch grass.
We work in Software ENGINEERING. Engineering is all about what tools makes sense to solve a specific problem. In some cases, AI tools do show immediate business value (eg. TTS for SDR) and in other cases this is less obvious.
This is all the more reason why learning about AI/ML fundamentals is critical in the same way understanding computer architecture, systems programming, algorithms, and design principles are critical to being a SWE, because then you can make a data-driven judgment on whether an approach works or not.
Given the number of throwaway accounts that commented, it clearly struck a nerve.
All these articles seem to think people will vibe code by prompting:
make me my own Stripe
make me my own Salesforce
make me my own Shopify
It will be more like:
Look at how Lago, an open-source Stripe layer, works and make it work with Authorized.net directly
Look at Twenty, an open-source CRM, and make it work in our tech stack for our sales needs
Look at how Medusa, an open-source e-commerce platform, works and what features we would need and bring into our website
When doing the latter, getting a good enough alternative will reduce the need for commercial SaaS. On top of that, these commercial SaaS are bloated with features in their attempt to work with as many use cases as possible and configuring them is “coding” by another name. Throw in Enshittification and the above seems to the next logical move by companies looking to move off these apps.
People are overestimating the value on having AI create something given loose instructions, and underestimating the value of using AI as a tool for a human to learn and explore a problem space. The bias shows on the terminology (“agents”).
We finally made the computer able to speak “our” language - but we still see computers as just automation. There’s a lot of untapped potential in the other direction, in encoding and compressing knowledge IMO.
Anyone who's seen an enterprise deal close or dealt with enterprise customer requests will know this, the build vs buy calculus has always been there yet companies still buy. Until you can get AI to the point where it equivalent to a 20 person engineering team, people are not going to build their own Snowflake, Salesforce, Slack or ATS. Maybe that day is 3 years away but when that happens the world will be very different
There was a short moment in history where it seemed that the sentiment was: people will soon 3D-print 99% of their household items themselves instead of buying them.
You absolutely could print things like cups, soap holders, picture frames, the small shovel you use for gardening, and so on an so on.
I just recreated most of Linear for my company in a few days. Making it hyper specific to what we want (metrics driven, lean startup style).
All state changes are made with MCP so it saved me from having to spend time on any forms and most interactions other than filtering searching sorting etc.
Means we will be ditching Linear soon.
I know I’m an outlier but this sort of thing will get more common.
> He said that software accounts for 8% to 12% of a company's expenses, so using vibe coding to build the company's resource planning or payroll tools would only save about 10%. Relying on AI to write code also carries risks, he said.
> "You have this innovation bazooka with these models. Why would you point it at rebuilding payroll or ERP or CRM," Acharya said
> Instead, companies are better off using AI to develop their core businesses or optimize the remaining 90% of their costs
The bottleneck will always be humans. You could get AI to write a million lines of code a day, but you’d still need humans to review and test that code. We are a very long way from being able to blindly trust AI’s outputs in production.
> "You have this innovation bazooka with these models. Why would you point it at rebuilding payroll or ERP or CRM"
They invested in ERP/CRM? I built one (fairly complete to the German/Italy/EU tax system) and it saves a ton of money vs commercial offerings. So yeah, of course we will.
I once built a CRM in Google Sheets fully mirroring the data model of Salesforce. For contact, company, deal, and call tracking for a one sales rep business. (Before XLookup was in Google Sheets)
Did it work? Yes. Was it worth my time to maintain and scale the “platform” with the company rather than outsource all that to a CRM company? Not at all.
Time is finite. Spend your time doing what you do best, pay others to do what they do best.
Thought exercise for those in disagreement: why would every company use AI to build their own payroll/ERP/CRM, when just a handful of companies could use AI to build those offerings better?
This is largely how things work now; AI may lower the cost and increase margins, but the economics of build vs buy seem the same.
I sort of agree with this, but what a lot of people are missing is it's unbelievably easy to clone a lot of SaaS products.
So I think big SaaS products are under attack from three angles now:
1) People replacing certain systems with 'vibe coded' ones, for either cost/feature/unhappiness with vendor reasons. I actually think this is a bigger threat than people think - there are so many BAD SaaS products out there which cost businesses a fortune in poor features/bugs/performance/uptime, and if the models/agents keep improving the way they have in the last couple of years it's going to be very interesting if some sort of '1000x' engineer in an agent can do crazy impressive stuff.
2) Agents 'replacing' the software. As people have pointed out, just have the agent use APIs to do whatever workflow you want - ping a database and output a report.
3) "Cheap" clones of existing products. A tiny team can now clone a "big" SaaS product very quickly. These guys can provide support/infra/migration assistance and make money at a much lower price point. Even if there is lock in, it makes it harder for SaaS companies to keep price pressure up.
"You have this innovation bazooka. Why would you point it at rebuilding payroll?" — a partner at the firm whose thesis was literally "software is eating the world."
Apparently the meal is over and now we're just rearranging the plates.
It seems to be premised on the idea we would vibe code a replica of what we get from SaaS. But the real point is, we would not do that. We would vibe code something that exactly fits our business.
We have products we're paying $100k a year for and using 3% of the functionality. And they suck. This is the target.
Using vibe coding to build a small specialized tool for a small company that can be used instead of single feature of a commercial SaaS is doable and brings value.
Using vibe coding to build something to replace an enterprise SaaS offering for a medium to large company is not something to be taken lightly. The tool and the code is not everything. The operating environment, security guarantees, SLAs, support, and a bag of features you don't need today but might tomorrow is what the SaaS offerings bring to the table.
Imagine that I run a really good software house. I can literally build anything you want, feature wise, better than most. I do it quickly. You come to me and say you want to replace Slack for your team of 200, because Slack got too expensive. I say I can do it. Because I am feeling generous and you're my good friend, I will do it for free. However, I will just give you the code, a CI/CD script, and a README.md file. I will disappear and will not maintain or support your software, nor will I give you any guarantees on how well it will work, other than a "trust me."
AI assisted coding is going to make it easier to create software. Developers will be more productive. Non developers will be able to create some stuff.
What this means is that very simple apps will become easy to create quickly. So a todo manager is probably not going to be a very successful business. You’ll be competing with many many people and it will be commoditised.
But ultimately what happens here is the “complexity threshold” of a sufficiently complex product needed to make money will be raised. Existing products will become more sophisticated or, if there is not more “sophistication ladder to climb” then they will be commoditised.
There’s just no way people are going to vibe all their software, that’s a very self absorbed nerd take. But on the supply side we’ll see commoditisation, price drops, and increasingly good value for the user as features are shipped faster.
I also think that software quality is really going to tank, because using validation to test the output of Claude is not a good way to ensure quality or correctness. It’ll get you some of the way but you need powerful reasoning. The most obvious evidence for this is security flaws in AI code. We’ll see a new era of enshitification caused by AI code. Like outsourced manufacturing though, people will buy worse stuff at a cheaper price. That makes me sad, because I thought we were on a path to better software, not buggier software.
Using non-deterministic LLMs to vibe code applications is a “workshop” activity - meaning it is iterative and the greater the complexity, the more complex it is to workshop the fixes. At some stage you realize that while you’re blown away by how sophisticated the LLM is, playing go-fish with it is a larger time suck than you expected. It’s like being asked to read a friend’s term paper only to find it’s beyond saving and you just say “good job”. Now scale this up to ever evolving, highly complex systems that have to work with five-nines regularity. When we don’t need the humans to workshop the fixes, things will be different.
Score Breakdown
-0.15
PreamblePreamble
Medium P: Data collection infrastructure
Editorial
ND
Structural
-0.15
SETL
ND
Combined
ND
Context Modifier
ND
Page structural elements reveal extensive tracking and consent management frameworks that indicate collection of personal data at scale, contrary to dignity and privacy implicit in preamble values.
0.00
Article 1Freedom, Equality, Brotherhood
Low
Editorial
ND
Structural
0.00
SETL
ND
Combined
ND
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ND
No observable content addressing equality or inherent dignity.
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Article 2Non-Discrimination
Low
Editorial
ND
Structural
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SETL
ND
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ND
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ND
No observable discrimination or rights-denial signals in page structure or title.
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Article 3Life, Liberty, Security
Low
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ND
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No content addressing right to life, liberty, or security.
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Article 4No Slavery
Low
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ND
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Article is business/tech commentary; no slavery or servitude references.
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Article 5No Torture
Low
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ND
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No torture or cruel treatment content observable.
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Article 6Legal Personhood
Low
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ND
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Right to legal recognition not addressable; commercial media article.
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Article 7Equality Before Law
Low
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No observable content on equal protection before law.
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Article 8Right to Remedy
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No effective remedy content visible.
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Article 9No Arbitrary Detention
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No arbitrary detention content.
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Article 10Fair Hearing
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No fair trial content addressable.
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Article 11Presumption of Innocence
Low
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No criminal law or retrospective application content.
-0.30
Article 12Privacy
High P: Third-party tracking P: Consent management infrastructure P: Behavioral data collection
Editorial
ND
Structural
-0.30
SETL
ND
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ND
Context Modifier
ND
Structural analysis reveals TCF, GPP, Didomi, analytics integration, and pixel-level tracking. These systems enable extensive monitoring of user behavior, location, and identity without explicit per-action consent. Contradicts right to privacy and protection against arbitrary interference.
+0.10
Article 13Freedom of Movement
Medium P: Free public access
Editorial
ND
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+0.10
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Public URL suggests article available to general audience without restriction; supports freedom of movement and information access.
0.00
Article 14Asylum
Low
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Asylum and refugee content not addressable in this tech/business article.
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Article 15Nationality
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Article 16Marriage & Family
Low
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Marriage and family content not relevant to tech opinion piece.
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Article 17Property
Low
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Property rights not directly addressable.
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Article 18Freedom of Thought
Low
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ND
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Freedom of thought, conscience, religion not observable in article preview.
+0.15
Article 19Freedom of Expression
Medium F: Public discourse on technology P: Free access publishing model
Editorial
+0.20
Structural
+0.10
SETL
+0.14
Combined
ND
Context Modifier
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Article title indicates publication of opinion/commentary on AI/coding technology; free access supports information access. However, business/commercial context and proprietary tracking limit editorial independence signal.
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Article 20Assembly & Association
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Article 21Political Participation
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Article 22Social Security
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Article 23Work & Equal Pay
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Labor rights not addressed in tech opinion article.
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Article 24Rest & Leisure
Low
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Rest and leisure not observable.
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Article 25Standard of Living
Low
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Article 26Education
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Article 27Cultural Participation
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Article 28Social & International Order
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Article 29Duties to Community
Low
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Article 30No Destruction of Rights
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