> Harnesses go from internal tooling you’d happily buy to something you’d no more outsource than your product-eng org or your GTM team.
I couldn't have said this better myself. If you're not owning your harness, and you're not owning your model, then that leaves very little moat for any AI native company.
Who ever wrote this it seems it never built any serious software. Software factories don’t exist and you will put yourself in a corner in a dangerous position
I’ve built a software factory and am using it to build an open-source alternative to every SaaS. What do you mean it’s not possible? A harness like Pi can be turned into a software factory with the right spec, extensions, skills, and a cron.
I opened https://marketplace.openship.org
and tried the "find me a t shirt" example prompt, added one to cart, i clicked once and it was not working, so i clicked a second time and it added two of them, so i realized every interaction was super slow. Congratulations for your slop automated platform, and good luck having customers
Holy moly every UI interaction on the cart is getting half a second to more than a second -.-'. Selecting a radio option freezes the ui for 0.5s. This is more than a vibe coded app
You do realize the marketplace is just a thin client over the stores? So yes you interacting with the marketplace hits the stores API. And the stores are demos that are in 1 region (US East). If you are not in the US, latency will be high. For production stores, CDNs will fix this issue.
Just went thru the full check out in incognito mode and it works for me.
If it chugs that badly with one user and zero load, I'm gonna assume it'll tip over if I so much as look at it funny.
SaaS is not the website itself. It's paying for risk management. For example, why did you use AWS instead of the wayyyy cheaper server-under-a-desk? At your scale, I bet it would be more than adequate.
If _you_ don't want to self-host something core to your business, think about why. Congrats, you now understand why people pay for SaaS.
Did you try it for yourself? The reason it's a bit finicky is that a lot of the actions in the chat skip the AI altogether, such as changing the cart values or changing the shipping options. In many other agentic commerce chat interfaces, every single action just involves sending a message in the chat. And then the AI calls the MCP action. But we thought, why not let some actions use the MCP tool directly? But this adds latency because you're interacting with the store's GraphQL API directly.
It’s still working for me and snappy but I’m in the region it’s hosted it. It’s also hosted on Railway so not sure where you got AWS from.
You don't gotta justify yourself. I don't really care why it's slow nor why you made the choices you made. And that's the point I was making: _neither will anyone else_. "It works on my machine" doesn't cut it when people are trying to determine if your stuff is worth the monumental risk.
I saw us-east and assumed it was us-east-1 but it doesn't matter. The same criticism applies to any hosting situation. You _could_ have gone with a cheaper DIY solution but didn't.
Also the software factory is actually making the B2B SaaS for me. The marketplace will interface with them so that comes a bit later. To judge the quality of the software factory, you can ask Claude to look at our Openfronts on GitHub, primarily the e-commerce and restaurant ones.
This couldn't be more true. Companies will be driven by AI harnesses (as defined by this article) that automate decision-making and prioritization. In the medium term, may the company with the best harness win.
In the longer term, the downstream impact is massive commoditization of software and invalidation of most existing moats. Data moats are gone if you can simulate the data with AI. Even platform effects can be sidestepped if AI replaces one side.
In addition, while right now agile startups have the advantage, at some point the balance will start tilting towards whoever has the most tokens. Startups have a limited time window to have whatever impact in the world they are hoping to have, or to build a moat that won't be disrupted by AI, but there are few of them left in the world.
The upside is that when there is a lot of commoditization, then the consumer wins.
imo there are a few real moats left assuming no superintelligent RSI scenario
- capital, as money is scarce
- network effects, as human attention is scarce
- relationships, as human attention is scarce
- research talent, assuming there exists some field(s) that AI is unable to surpass the best researchers
- proprietary data and sensors, as systems of record and action are scarce (training data, on the other hand, can maybe we simulated, but I'm pretty bearish in general on the idea of fully simulated data)
Most microphones on professional stages are Shure microphones. They cost more than microphones that get equivalent reviews for performance. So why do audio professionals keep buying expensive Shure microphones? Because they have a proven track record of being extremely reliable. No one wants to be responsible for the mic failing during a performance. So people trust Shure to be reliable. Trust builds up over a long time and becomes what we call a brand.
Maybe stop to think why they are selling you tokens and the promise of being able to recreate and outcompete every business instead of just doing it themselves.
This couldn't be more true. Companies will be driven by AI harnesses (as defined by this article) that automate decision-making and prioritization. In the medium term, may the company with the best harness win. In the longer term, the downstream impact is massive commoditization of software and invalidation of most existing moats. Data moats are gone if you can simulate the data with AI. Even platform effects can be sidestepped if AI replaces one side of the platform.
I wonder what else is there left to be invented. Surely we can optimize what we have, but what is the "next frontier"? Feels like we are stuck, not constrained but void of new ideas.
All harnesses are basically the same. Why wouldn't there be a few large harnesses that everyone uses to run the whole business? In other words, why would there be a plethora of small harnesses and not a few big ones?
> If this is the right mental model, you should expect to see:
> An unusual amount of in-house harness building on both the build side and the sell side
Yup.
> Org structures and individual roles being reshaped around their place in the business harness
Not yet, this is the one I am most skeptical about because it's still way too easy for the harness-driven processes I've seen to go off the rails, so it's still very much human SME-driven. However, the number of butts in seats required is going down.
> AI-native startups beating incumbents in domains where the “moat” can be easily harness-ified
In most cases I've seen the "moat" is not easily harness-ified, and increasingly the money and energy seems to be going into getting to the starting line.
> All software a software company uses (on or tied to the core build or sell paths) needing to be headless so the outer harness can run it
Tech companies are definitely doing it, but I don't think this is actually realistic yet for companies whose core competence isn't software. But this feels like a matter of when, not if.
I am watching completely non tech companies using AI to replace entire weeks of work and effort. The customer makes a phone call, and it is AI from the intake to discovery to documents to contract and negotiations to AI delivery to the notary. That work is a week of back and forth replaced with a few agents and $30/month.
But they greatly, greatly overestimate people, that for the most part, are burnt out and have real life priorities that supercede monitoring whatever the fuck an agent swarm is. Obviously.
The need for more compute, in the orders of many multiples in magnitude, is still needed before we even get anywhere near there. And the cost is still too high for what it is now, which is basically a science experiment after the first threshold of complexity.
Many businesses are going to be more about who accepts liability for work product than who makes the work product. You can't sue codex for breach of contract when your customer database wipes.
Wait a minute - and who is going to buy that software exactly?
Because someone was so sick of expensive Adobe Photoshop they created their own Vibe coded PhotoShop for $2000 and then selling it on for way cheaper. [0]
Some YouTuber got so sick of Adobe Premier they literally vibe coded their own full fledge video editor exactly to their needs with everything else that they don't need stripped out. [1]
I know of a case where a totally non technical person sitting in an south asian city wrote his own financial management software for his business in just three weeks. Vibe coded and now his daily driver tracking accounts, payables, receivables, contracts, parses PDFs from his inbox, populates forms and full workflow that HE needs for his specific commission/resale/distribution business.
So what this company is going to sell with that Harness? Or would it be selling just that Harness to other companies like those Rails bootstrap SaaS boilerplate businesses that used to charge $300 for that junk with lifetime update promise?
I think you hit it spot on. Developers and software companies have inherently thought the goal is delivery of features, because that is what wins new business, but in reality, it is utilization of those features showing impact that actually matters.
You can never prove that there aren't any bugs. The definition can also change according to time and expectations.
I've made tens of internal developer tools because I couldn't gaf about using some third party vendor, all before vibe coding. Even then we purchased software from vendors, and we still do it now.
You can't cherry pick 2-3 pieces of problematic software, imply that it's how everything works. You're vibe coded Photoshop will never be used seriously, unfortunately. Same goes for the video editor, if you're fine with an autonomous AI agents listening to some internal github repo for issues that get auto or human reported and attempting to provide live fixes and feature enrichment, you aren't going to get that.
The problem really does stem from the fact that these agents, no matter how good you keep making them are still limited by a time horizon. AI companies have gotten really good at extending that horizon. The contexts get longer, prompts get less specific but theres a deadline to all of this. Take that LLM point it to a specific problem let it run, pull it out start new. The LLM will inevitably accumulate technical debt in it's own context, that starts to create a rot. You can try to improve this problem by making sub agents, etc. but you will 100% run into context rot after a period of time for the lead agent.
A lot of (non-software) business is already like this, without the AI part.
There are 40,000 McDonald’s franchisees who basically just put money into a coinop machine and the machine returns with exactly the food, drinks, restaurant decor, prices, etc that it wants them to sell.
Toyota’s production system coordinates what gets made and when, detects abnormalities and directs human attention toward problems and improvement. They explicitly eliminate the need for people to continuously watch machines, while preserving human judgment
I think you just precisely laid out why replacing SaaS with AI doesn't work.
Every contract is different and the majority of the work is "the boring inefficient stuff" that can't be automated away. That is, arguing with shifty and arrogant middle management in a long series of meetings until you get some concrete requirements. Then realizing the metric shit ton of crap to untangle and test rigorously. It's not just a matter of having patience, but being unflinchingly attentive the whole time for the nuggets of gold that fall out of their disgusting mouths and then pouncing with a plan so you don't go over time and budget. AI simply isn't going to grill people like that and then execute swiftly and precisely. A lot of these jobs really are like a murder interrogation and then finding the bodies.
It's absurd that so many people believe there are unturned stones in this space. If this kind of business could be cookie-cutter, it would have happened over a decade ago and it wouldn't have made any money.
Correct me if I'm wrong, but I think this argument hinges on AI not really being intelligent.
If we're talking automation in the way that humans define a process, boundary cases, etc up front then yes you can't really automate away one-off monotony.
If AI is actually artificial intelligence, it will be able to figure that out similar to how a human would.
I compared it to interrogation because it's about steering the conversation. The only reason you're talking to middle management is because their work is getting outsourced to SaaS.
If you waste too much time on irrelevant topics, you lose. If you don't collect enough information, you lose. If the middle manager feels threatened, you lose. If you don't have a good production release, you lose. If there are any high severity bugs, you lose. If there are too many bugs, you lose.
The only way to win is to carve an exact path through the mess from the beginning, and that needs human experience.
Maybe we just have different ideas of what AI would be?
I don't consider an impressive text predictor to be artificial intelligence. Maybe that means LLMs aren't AI, I honestly don't know because no one seems to care about understanding how they work or solving interpetability first.
I do expect anything that earns the banner of AI could ask good questions, weed through a bunch of word vomit to find the key nuggets, and act on them similar to or better than a human could. Anything less than that doesn't seem particularly intelligent.
But I believe a premise of the article is that AI-ification will disrupt the power of those middle managers, probably in a few different ways.
One is that the middle managers themselves will be replaced by AI. Another is that more purchasing decisions will be made by engineers building the corporate harness on the buyer side. And, probably people will be more accustomed (i.e. forced) to talk to AIs on the other side, or at least accept that the responses are ultimately constrained by them. Going out for a round of golf with the sales guy just can't accomplish anything anymore.
Every exec team wants this transition to happen, so both buy and sell side will go through great transformation in concert.
Corporations are harnesses over property, logistics, knowledge, processes, and capital.
The harness controls the AI input and output into an outcome that requires judgement.
The human part is that judgement. Not the decisions - we can pass that off but what is a 'good' thing?
Tomatoes in your fruit salad? Tomatoes are fruit. Olive oil in your engine? Lubricants are lubricants.
AI slop creates software and documents and websites very quickly. It passes the tests. It does the thing but can it be trusted? Consistent positive judgement calls create a track record and that creates trust.
An uninspired vision of the future. We have endless evidence through history that hiring the most engineers, producing the most output, raising the most money is not the path to success. The most successful businesses are those that deeply understand something about their customers, about how to sell to their buyers. The most has never mattered. An army of agents doing endless busywork is not the path to success.
Google isn’t successful because it has the best engineers, it has the best engineers because it is successful. Google is an extremely boring business: show adverts to make money. And they made so much money. The next Google is not going to be the company with the best “harness” it’ll be the company that has an obscenely profitable product.
Founder mode, what was considered a panacea just 12 months ago, is defined by a founder giving a shit about everything. You don’t succeed by handing everything off to an army of ~agents~ consultants.
This vision of the future is nonsense that will not pan out. You can add that to the HN AI predictions. At no point will real businesses be “harnesses” around AI models.
I don't think your take is counter to the article. It is precisely the "founder giving a shit about everything" that will be amplified the most by a capable AI harness, because AI reduces the power of capital (e.g. hiring a lot of good engineers or buying a lot of data or ads) as a moat by amplifying good judgement.
(This is temporary until the AI gets better judgement than humans, then capital will therever be the most powerful moat in a market full of dystopic, consequentialist, incredibly long-sightedly-greedy companies)
The article is arguing that a company’s output will be entirely created by AI (via the harness) and only the input (developing the harness) will be human. My take is counter to that. My take is that success in business comes from the small important details, not the volume of output. You can develop a harness to produce 1000x more output than humans but that doesn’t matter because even today, most businesses fail, not because they didn’t produce enough output, but because their output was wrong.
Long before A.I., tiny 10 person startups have been able to revolutionize industries.
I didn't read it this way. I think I equated it most to "culture" in current companies. A culture that fosters excellence is necessary (but not sufficient) to have a great company. There are also many types of cultures that have been shown to work (ie there's not one right answer).
The article argues (and I think I agree) that how you choose to incorporate agents into your work will be a differentiator and most great companies will have a unique take on it.
This is a side issue, but he mentions that the harness is stateless. But no agentic task is actually stateless.
I suspect for purposes of optimization and performance etc., sometime in the next several months we will start to see new popular ML architectures or variations of LLMs/VLMs that are designed to be _stateful_. So a lot of the infrastructure for managing state and memory etc. gets sucked into the model somehow.
They may end up changing or expanding the concept of an ML model to enable that.
That type of belief is what is making me anxious about AI engineering staying relevant for much longer. I just wonder if pretty soon we need to be able to build and train or customize ML models that just do everything, or at least know how to prompt an agent to do that.
I've heard some variant of the SaaS doomsday prophecy dozens of times over the past year. Somehow, SaaS is still here and more or less looks the same.
It will probably continue to exist because most businesses are perfectly content to outsource tech problems to someone else for a reasonable fee, and if you think that managing an agent swarm is a seamless replacement for that, I don't know what you tell you. The vast majority of business owners don't want the added complexity in their life.
And for the same reason, most SaaS will probably continue to exist largely unchanged for the next couple of years. Some of it will be vibecoded, but the UX will probably be deterministic. I think this might change once a new breed of "LLM-native" business owners takes charge, but that's going to be a slow process, and it will be hampered by prosaic concerns about interoperability, support, predictable cost, and liability for mistakes (from "the agent did a bit of tax fraud" to "the agent decided to hack my competition").
As a general rule, expecting prophesies to be realized or invalidated within a year is pretty silly.
That said, your point can be distilled down to: comparative advantage. It still exists and so SaaS will likely still exist. But per the article, it'll probably look pretty different (and a lot less profitable) over time.
I think the reason it'll look different is that it's very hard to make powerful software that's also walk-up usable. The idea that you can provide just the core concepts of a piece of software and allow AI to interact with those concepts more directly, or allow your users to use AI to build their ideal (and ever-changing) interfaces to your concepts seems pretty convincing to me.
Again, barring radical superhumanity, in many cases companies do not today want their own bespoke solutions to a problem and wouldn't want it even if the developers were working for free, because the other costs to the business would still be more than they want to pay and more than another business can charge to make the problem more thoroughly (even if not completely) go away.
I suspect this is another reason you may see some companies making grabs at data and points-of-presence that otherwise make little or no sense... they're trying to colonize and defend the sources of contact with the real world before someone else gets there and locks them out.
It’s exactly that kind of boring plumbing that companies want to outsource which is the exact opposite of what near term AI is good at. So yes there’s many SaaS companies that should be concerned, but suggesting all SaaS is ending anytime soon is pure hype nonsense.
AI reduces the marginal cost of solving some of these problems. IE some will roll their own solutions, some will roll their own partial solutions. the very really will be to reduce the total market for some SaaS, and add pricing and margin pressure.
Saaspocalypse is overstated. but some kind of changes are coming.
I feel like you are responding to something that's not in the article.
It doesn't say SaaS is being replaced by agents, rather that SaaS companies will be transformed in the way described. Outsourcing will still happen, hence the discussion of headless components, and the continued demand for SaaS.
And part of the argument is that we won't just be waiting for the new breed of LLM-native companies; lots of existing businesses are being LLM-ified right now, through the process described in the article...
I think any scenario in which SaaS remains a pervasive business model involves either LLMs not ultimately being that transformative or significant worsening of dystopian trends already in progress. If, as many people anticipate, software written with LLMs becomes reliable enough to be trusted, there's little moat to be had from software alone.
Does this mean they will run everything through an LLM? Besides the cost, does this mean the whole business will become non-deterministic? I can see using LLM to produce code that the business runs on (and can be tested), but running the business directly on an LLM seems to be asking for massive trouble.
SaaS will die back, but not because of this. However, if we entertain this painfully silly idea, simply consider this: if script wrappers around some genAI can replace a SaaS you not longer need to pay for the aaS.
Regardless, the slop injection into most major SaaS providers has already begun to cost them customers, so hopefully more businesses will remember you can just higher some humans for a more predictable cost and personalized results with support that knows your system. SaaS was a mistake, much like genAI hype.
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[ 3.8 ms ] story [ 77.9 ms ] threadI couldn't have said this better myself. If you're not owning your harness, and you're not owning your model, then that leaves very little moat for any AI native company.
Just went thru the full check out in incognito mode and it works for me.
SaaS is not the website itself. It's paying for risk management. For example, why did you use AWS instead of the wayyyy cheaper server-under-a-desk? At your scale, I bet it would be more than adequate.
If _you_ don't want to self-host something core to your business, think about why. Congrats, you now understand why people pay for SaaS.
It’s still working for me and snappy but I’m in the region it’s hosted it. It’s also hosted on Railway so not sure where you got AWS from.
I saw us-east and assumed it was us-east-1 but it doesn't matter. The same criticism applies to any hosting situation. You _could_ have gone with a cheaper DIY solution but didn't.
I’m curious what you mean by a cheaper DIY solution?
Software factories don't exist is an easily falsifiable statement https://github.com/topics/software-factory
I feel this is probably coming and perhaps inevitable, but I fear for the poor employees this sort of thing will be tried out on first.
Also https://web.archive.org/web/20260725174355/https://marshallb...
My current project is created by one-shot prompts. That’s not some kind of parlor trick. It is the framework for creating and evolving the product.
Many people laugh it off as “unserious”. But as I said, I see this happening in my place of work where people are paid lots of money.
https://jaisenmathai.com/articles/sojourn-for-ios-was-45-one...
In the longer term, the downstream impact is massive commoditization of software and invalidation of most existing moats. Data moats are gone if you can simulate the data with AI. Even platform effects can be sidestepped if AI replaces one side.
In addition, while right now agile startups have the advantage, at some point the balance will start tilting towards whoever has the most tokens. Startups have a limited time window to have whatever impact in the world they are hoping to have, or to build a moat that won't be disrupted by AI, but there are few of them left in the world.
The upside is that when there is a lot of commoditization, then the consumer wins.
This is a hilarious premise if you work in a domain where it matters even a little bit whether the data is correct or not.
- capital, as money is scarce
- network effects, as human attention is scarce
- relationships, as human attention is scarce
- research talent, assuming there exists some field(s) that AI is unable to surpass the best researchers
- proprietary data and sensors, as systems of record and action are scarce (training data, on the other hand, can maybe we simulated, but I'm pretty bearish in general on the idea of fully simulated data)
If AI levels out most other distinctions, maybe in the end we choose to give money to people we trust.
Most microphones on professional stages are Shure microphones. They cost more than microphones that get equivalent reviews for performance. So why do audio professionals keep buying expensive Shure microphones? Because they have a proven track record of being extremely reliable. No one wants to be responsible for the mic failing during a performance. So people trust Shure to be reliable. Trust builds up over a long time and becomes what we call a brand.
I wonder what else is there left to be invented. Surely we can optimize what we have, but what is the "next frontier"? Feels like we are stuck, not constrained but void of new ideas.
But to pull something from the post:
> If this is the right mental model, you should expect to see:
> An unusual amount of in-house harness building on both the build side and the sell side
Yup.
> Org structures and individual roles being reshaped around their place in the business harness
Not yet, this is the one I am most skeptical about because it's still way too easy for the harness-driven processes I've seen to go off the rails, so it's still very much human SME-driven. However, the number of butts in seats required is going down.
> AI-native startups beating incumbents in domains where the “moat” can be easily harness-ified
In most cases I've seen the "moat" is not easily harness-ified, and increasingly the money and energy seems to be going into getting to the starting line.
> All software a software company uses (on or tied to the core build or sell paths) needing to be headless so the outer harness can run it
Tech companies are definitely doing it, but I don't think this is actually realistic yet for companies whose core competence isn't software. But this feels like a matter of when, not if.
But they greatly, greatly overestimate people, that for the most part, are burnt out and have real life priorities that supercede monitoring whatever the fuck an agent swarm is. Obviously.
The need for more compute, in the orders of many multiples in magnitude, is still needed before we even get anywhere near there. And the cost is still too high for what it is now, which is basically a science experiment after the first threshold of complexity.
Because someone was so sick of expensive Adobe Photoshop they created their own Vibe coded PhotoShop for $2000 and then selling it on for way cheaper. [0]
Some YouTuber got so sick of Adobe Premier they literally vibe coded their own full fledge video editor exactly to their needs with everything else that they don't need stripped out. [1]
I know of a case where a totally non technical person sitting in an south asian city wrote his own financial management software for his business in just three weeks. Vibe coded and now his daily driver tracking accounts, payables, receivables, contracts, parses PDFs from his inbox, populates forms and full workflow that HE needs for his specific commission/resale/distribution business.
So what this company is going to sell with that Harness? Or would it be selling just that Harness to other companies like those Rails bootstrap SaaS boilerplate businesses that used to charge $300 for that junk with lifetime update promise?
[0]. https://www.reddit.com/r/vibecoding/comments/1wpaies/my_vibe... [1]. https://youtu.be/qOQ3MEx6czY?si=f-XBBri5x8hpPHWa
You can never prove that there aren't any bugs. The definition can also change according to time and expectations.
I've made tens of internal developer tools because I couldn't gaf about using some third party vendor, all before vibe coding. Even then we purchased software from vendors, and we still do it now.
You can't cherry pick 2-3 pieces of problematic software, imply that it's how everything works. You're vibe coded Photoshop will never be used seriously, unfortunately. Same goes for the video editor, if you're fine with an autonomous AI agents listening to some internal github repo for issues that get auto or human reported and attempting to provide live fixes and feature enrichment, you aren't going to get that.
The problem really does stem from the fact that these agents, no matter how good you keep making them are still limited by a time horizon. AI companies have gotten really good at extending that horizon. The contexts get longer, prompts get less specific but theres a deadline to all of this. Take that LLM point it to a specific problem let it run, pull it out start new. The LLM will inevitably accumulate technical debt in it's own context, that starts to create a rot. You can try to improve this problem by making sub agents, etc. but you will 100% run into context rot after a period of time for the lead agent.
There are 40,000 McDonald’s franchisees who basically just put money into a coinop machine and the machine returns with exactly the food, drinks, restaurant decor, prices, etc that it wants them to sell.
Toyota’s production system coordinates what gets made and when, detects abnormalities and directs human attention toward problems and improvement. They explicitly eliminate the need for people to continuously watch machines, while preserving human judgment
Every contract is different and the majority of the work is "the boring inefficient stuff" that can't be automated away. That is, arguing with shifty and arrogant middle management in a long series of meetings until you get some concrete requirements. Then realizing the metric shit ton of crap to untangle and test rigorously. It's not just a matter of having patience, but being unflinchingly attentive the whole time for the nuggets of gold that fall out of their disgusting mouths and then pouncing with a plan so you don't go over time and budget. AI simply isn't going to grill people like that and then execute swiftly and precisely. A lot of these jobs really are like a murder interrogation and then finding the bodies.
It's absurd that so many people believe there are unturned stones in this space. If this kind of business could be cookie-cutter, it would have happened over a decade ago and it wouldn't have made any money.
If we're talking automation in the way that humans define a process, boundary cases, etc up front then yes you can't really automate away one-off monotony.
If AI is actually artificial intelligence, it will be able to figure that out similar to how a human would.
If you waste too much time on irrelevant topics, you lose. If you don't collect enough information, you lose. If the middle manager feels threatened, you lose. If you don't have a good production release, you lose. If there are any high severity bugs, you lose. If there are too many bugs, you lose.
The only way to win is to carve an exact path through the mess from the beginning, and that needs human experience.
I don't consider an impressive text predictor to be artificial intelligence. Maybe that means LLMs aren't AI, I honestly don't know because no one seems to care about understanding how they work or solving interpetability first.
I do expect anything that earns the banner of AI could ask good questions, weed through a bunch of word vomit to find the key nuggets, and act on them similar to or better than a human could. Anything less than that doesn't seem particularly intelligent.
But I believe a premise of the article is that AI-ification will disrupt the power of those middle managers, probably in a few different ways.
One is that the middle managers themselves will be replaced by AI. Another is that more purchasing decisions will be made by engineers building the corporate harness on the buyer side. And, probably people will be more accustomed (i.e. forced) to talk to AIs on the other side, or at least accept that the responses are ultimately constrained by them. Going out for a round of golf with the sales guy just can't accomplish anything anymore.
Every exec team wants this transition to happen, so both buy and sell side will go through great transformation in concert.
The harness controls the AI input and output into an outcome that requires judgement.
The human part is that judgement. Not the decisions - we can pass that off but what is a 'good' thing?
Tomatoes in your fruit salad? Tomatoes are fruit. Olive oil in your engine? Lubricants are lubricants.
AI slop creates software and documents and websites very quickly. It passes the tests. It does the thing but can it be trusted? Consistent positive judgement calls create a track record and that creates trust.
This explains the anticipation about Jev.
Trust is now what sells to the highest bidder.
Google isn’t successful because it has the best engineers, it has the best engineers because it is successful. Google is an extremely boring business: show adverts to make money. And they made so much money. The next Google is not going to be the company with the best “harness” it’ll be the company that has an obscenely profitable product.
Founder mode, what was considered a panacea just 12 months ago, is defined by a founder giving a shit about everything. You don’t succeed by handing everything off to an army of ~agents~ consultants.
This vision of the future is nonsense that will not pan out. You can add that to the HN AI predictions. At no point will real businesses be “harnesses” around AI models.
(This is temporary until the AI gets better judgement than humans, then capital will therever be the most powerful moat in a market full of dystopic, consequentialist, incredibly long-sightedly-greedy companies)
Long before A.I., tiny 10 person startups have been able to revolutionize industries.
Why wouldn’t capital simply lose its value even further, especially as capital globalizes further
The article argues (and I think I agree) that how you choose to incorporate agents into your work will be a differentiator and most great companies will have a unique take on it.
I suspect for purposes of optimization and performance etc., sometime in the next several months we will start to see new popular ML architectures or variations of LLMs/VLMs that are designed to be _stateful_. So a lot of the infrastructure for managing state and memory etc. gets sucked into the model somehow.
They may end up changing or expanding the concept of an ML model to enable that.
That type of belief is what is making me anxious about AI engineering staying relevant for much longer. I just wonder if pretty soon we need to be able to build and train or customize ML models that just do everything, or at least know how to prompt an agent to do that.
New technologies will always lower the bar and raise the ceiling.
We may not be able to see the ceiling yet while still learning capabilities and implementations 5-20 years from now.
Also, I can't help but laugh a little that for the most part, text files are being called a harness.
It will probably continue to exist because most businesses are perfectly content to outsource tech problems to someone else for a reasonable fee, and if you think that managing an agent swarm is a seamless replacement for that, I don't know what you tell you. The vast majority of business owners don't want the added complexity in their life.
And for the same reason, most SaaS will probably continue to exist largely unchanged for the next couple of years. Some of it will be vibecoded, but the UX will probably be deterministic. I think this might change once a new breed of "LLM-native" business owners takes charge, but that's going to be a slow process, and it will be hampered by prosaic concerns about interoperability, support, predictable cost, and liability for mistakes (from "the agent did a bit of tax fraud" to "the agent decided to hack my competition").
That said, your point can be distilled down to: comparative advantage. It still exists and so SaaS will likely still exist. But per the article, it'll probably look pretty different (and a lot less profitable) over time.
I think the reason it'll look different is that it's very hard to make powerful software that's also walk-up usable. The idea that you can provide just the core concepts of a piece of software and allow AI to interact with those concepts more directly, or allow your users to use AI to build their ideal (and ever-changing) interfaces to your concepts seems pretty convincing to me.
Again, barring radical superhumanity, in many cases companies do not today want their own bespoke solutions to a problem and wouldn't want it even if the developers were working for free, because the other costs to the business would still be more than they want to pay and more than another business can charge to make the problem more thoroughly (even if not completely) go away.
I suspect this is another reason you may see some companies making grabs at data and points-of-presence that otherwise make little or no sense... they're trying to colonize and defend the sources of contact with the real world before someone else gets there and locks them out.
It’s exactly that kind of boring plumbing that companies want to outsource which is the exact opposite of what near term AI is good at. So yes there’s many SaaS companies that should be concerned, but suggesting all SaaS is ending anytime soon is pure hype nonsense.
Saaspocalypse is overstated. but some kind of changes are coming.
It doesn't say SaaS is being replaced by agents, rather that SaaS companies will be transformed in the way described. Outsourcing will still happen, hence the discussion of headless components, and the continued demand for SaaS.
And part of the argument is that we won't just be waiting for the new breed of LLM-native companies; lots of existing businesses are being LLM-ified right now, through the process described in the article...
When the definition is that broad, aren’t you effectively already defining what SaaS is today.
Especially when you include interfaces in that definition.