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").
> 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.
Of course, nothing is really preventing the SaaS side from operating the agent swarm, yes?
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 isn't going to grill people like that and then execute swiftly. 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 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.
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.
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.
In addition, while right now agile startups have the advantage, at some point the balance will start tilting towards whoever has the most tokens (OR perhaps durable moats will trump even near-infinite tokens; we will have to see). 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 benefits.
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)
You are assuming that getting the AI to generate correct (or accurate, representative) data will be very difficult. I would agree, but I think it will become possible in the long run.
(Edit: alternatively you just use AI to get rid of the need for data to solve a problem, like Jev did for traditional classification models)
I think current incentives definitely go against any efforts to build this. It's very hard to build this and be rewarded for it by, say, investors or your boss, because you can't really prove that your system is non-sloppy while your competitor's is (even if being non-sloppy is all that matters), because by definition your novel results are not verifiable or else the model labs will have already trained it into their model.
But the same is true for high-quality AI systems in general. In general, I think AI model advancements will make the systems easier and easier to build until some small guy accountable to no one but themselvs can build it, and then it will actually be built.
1. He’s talking about training new models and at one point, having data was valuable. Now synthetic data is being used to train models
2. Companies like SalesForce who’s moat is having all your customer data so you’ll be locked in. You could extract it but you’d have to clean it and then change it to your new schema. With LLMs, you can do that in minutes and even use SalesForces MCP or API to get all your data and leave.
It’s exactly why companies like Figma are gate keeping their MCP. They know that swapping their MCP with Paper’s or any new one is easy.
The moats are evaporating as we speak. Distribution is one of the smaller ones left, but the personal software trend might eat that too.
> Distribution is one of the smaller ones left, but the personal software trend might eat that too
Being a platform for personal software is gonna be valuable, but it needs a lot of trust. (I have a nonprofit idea around this right now)
Btw, I think distribution might temporarily become less important (because with better AI you can actually pull so far ahead of competitors quality-wise and therefore succeed despite a distribution drawback), but long run it actually becomes more important because of AI persuasion and commodification? If you are the super app then, well, you are the super app
I work in science, the data is becoming far bigger of a moat than it ever was before because of this.
Every company can now apply the latest and greatest analysis. Data generation is where the cost is. It's where the time was spent, time that can never ever be retrieved at any cost.
AI won't solve biology, make a pathogenic virus, etc, without tons and tons of data, of both types we know and types we have not yet figured out how to generate.
Perhaps the area where AI has the most to help bio is in figuring out novel measurement technology. But it's not going to be able to reason or deep-net its way to figuring out systems for which we can't even measure the parts.
Yep private scientific knowledge is a massive new moat that you can pull if you simply invest in scientific discovery methods in general and throw enough resources and tokens at the problem. I think there are already startups specifically trying to do this. The main obstacle is whether you're actually able to pull significantly ahead of (AI-enabled) public science to make a difference, but I guess the math works out if you're sufficiently AGI-pilled.
I agree this is a kind of data moat, but it's also arguably distinct enough to be its own thing.
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 think in-house AI developer tools (like the ones we’ve seen from Ramp, Stripe, DoorDash, etc.) are the beginning of this.
And on this, these are examples of tech companies filled with nerds who love novel new technology. Of course companies like Ramp and Stripe are spending huge amounts of time and money on taking this new technology to the extremes with “harnesses” and ”factories” because that’s what the nerds want to do. What the nerds want to do is not a sign of how the technology will be used in future, it’s a sign of what is most fun today.
It's not always what the nerds want to do though: I know of what the insides used to look like in one of those on the list, and things were very custom, because some early technical decisions were outright landmines that were too expensive to move away from. So having custom harnesses to actually survive that codebase I remember seems like a baseline to get reasonable turnaround of AI helpers. The company had done cowboy development for way too long, and solved too many problems by creating bigger, more custom problems.
So don't assume it's all nerds having fun. Now, is it what the future will look like for everyone? I don't think so, but that's because I expect AI is bringing us a more unified dev experience, with more developers than even Google has. This will make general programming harnesses very good, quite fast, solely because said harnesses can be differentiators in the AI race. So we are seeing some of the largest companies inthe world, with the largest research budgets, dedicating more money to the dev experience of their product than almost anyone else does.
> 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.
You're talking about the best businesses. Arguably the article is talking about the mean of the "startup" tranche of businesses.
Google isn't a SaaS. That's (is or is tangential to) the kind of businesses this article is referring to. And they make decent money, they just aren't a unicorn that didn't die.
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.
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.
We have evidence that this is not true. For years and years you have been able to self-host Zulip and Mattermost but companies still pay Slack millions of dollars per year. GitHub has billions in revenue, why not just use Gitea for free? So on and so forth. The ability to generate a (shitty) clone of software does not magically make the cloned software less valuable. Paying for software isn’t a “tax” when it produces value. If this vision of the future was true, cheap/free alternatives would have already destroyed the SaaS market years and years ago.
I doubt that agents can make a product as polished as Slack. The status quo would indicate that's not the case as the prediction that SaaS would die has not panned out at all.
> The quality of Mattermost and Zulip was NOT exactly as polished as Slack.
Mattermost isn't actually all that bad and its mental model is pretty close to Slack, at least compared to Zulip. I actually prefer Mattermost to Teams, and would view it in the same ballpark as Slack.
> Deployment of such stacks was a problem. A complicate operational overhead.
I'd say that in Mattermost's case the group calling functionality was locked behind a subscription as well as some other stuff last I checked, which makes it dead on arrival for many. It was quite nice software though, even before being able to vibe code your own (though tackling videos sharing and RTMP will be anything but trivial).
> Make pixel perfect clone of Slack or Jira with SQLite or ejabberd behind.
There's A LOT of functionality and features in Jira, the only thing that saves claims like that is that you probably don't use 80% and can just build what your company needs. I more or less did that for the hell of it (MariaDB + Garage + Dropwizard/Java + Angular/Spartan) and there was still so much supervision an changes that were needed - after pulling the Jira DB scheme and throwing about 60% of it in the trash (didn't need the automation), there was still A LOT of stuff to do and even the plans eat up a whole bunch of the context of the coding tools you intend to use, no matter how many checks and scripts you write to automate guardrails for when the models simply don't recall everything that is relevant.
For people who want to self-host something basic, there's already the excellent https://kanboard.org/ (very very fast)
For those that want something similarly depressingly slow to Jira while still locking some stuff behind a subscription (at least in the versions I tried) there's the passable https://www.openproject.org/
If those don't fit and you don't see anything exactly like what you need, you might try to build your own. But yeah, not a weekend project.
> 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.
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 worth of tokens 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?
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.
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.
If AI development is like management, harnesses will totally end up orchestrating people like traditional management does. The end state of the Jev type model hype is that a few years from now decision models replace management decision. Disconnected human managers five levels away from the work make suboptimal decisions all the time, the same logic applied to self driving cars can play out here. The model doesn't need to be perfect, it just needs to make better informed decisions than your fallible VP.
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.
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?
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 had rented an office in the same building as them a few years back. Funny enough and unrelatedly, their VCs invited me for a meet at one point, so I researched their portcos and abnormal is one of them.
Their main thing, iirc, was detecting ~abnormal~ looking emails. It's the main way companies lose money and IP, it's not via crazy hacks, it's obviously the human element that is the biggest vulnerability in all companies. A stressed employee gets an email saying hey i'm the ceo i'm in a rush, or hey it's totally jennifer from your team i'm stuck outside, send me all the files/do x quickly. I don't have any fucking idea what they're doing now and I bet you they dont either
This must be a response to the cyber stuff from the big labs
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").
> 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.
Of course, nothing is really preventing the SaaS side from operating the agent swarm, yes?
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 isn't going to grill people like that and then execute swiftly. 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.
I go back and forth on this.
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.
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.
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.
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.
In addition, while right now agile startups have the advantage, at some point the balance will start tilting towards whoever has the most tokens (OR perhaps durable moats will trump even near-infinite tokens; we will have to see). 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 benefits.
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)
Trust. I trust the Debian maintainers. I don't trust OpenAI.
If AI levels out most other distinctions, maybe in the end we choose to give money to people we trust.
> Data moats are gone if you can simulate the data with AI.
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.
You are assuming that getting the AI to generate correct (or accurate, representative) data will be very difficult. I would agree, but I think it will become possible in the long run.
(Edit: alternatively you just use AI to get rid of the need for data to solve a problem, like Jev did for traditional classification models)
I think current incentives definitely go against any efforts to build this. It's very hard to build this and be rewarded for it by, say, investors or your boss, because you can't really prove that your system is non-sloppy while your competitor's is (even if being non-sloppy is all that matters), because by definition your novel results are not verifiable or else the model labs will have already trained it into their model.
But the same is true for high-quality AI systems in general. In general, I think AI model advancements will make the systems easier and easier to build until some small guy accountable to no one but themselvs can build it, and then it will actually be built.
There’s 2 arguments.
1. He’s talking about training new models and at one point, having data was valuable. Now synthetic data is being used to train models
2. Companies like SalesForce who’s moat is having all your customer data so you’ll be locked in. You could extract it but you’d have to clean it and then change it to your new schema. With LLMs, you can do that in minutes and even use SalesForces MCP or API to get all your data and leave.
It’s exactly why companies like Figma are gate keeping their MCP. They know that swapping their MCP with Paper’s or any new one is easy.
The moats are evaporating as we speak. Distribution is one of the smaller ones left, but the personal software trend might eat that too.
> Distribution is one of the smaller ones left, but the personal software trend might eat that too
Being a platform for personal software is gonna be valuable, but it needs a lot of trust. (I have a nonprofit idea around this right now)
Btw, I think distribution might temporarily become less important (because with better AI you can actually pull so far ahead of competitors quality-wise and therefore succeed despite a distribution drawback), but long run it actually becomes more important because of AI persuasion and commodification? If you are the super app then, well, you are the super app
I work in science, the data is becoming far bigger of a moat than it ever was before because of this.
Every company can now apply the latest and greatest analysis. Data generation is where the cost is. It's where the time was spent, time that can never ever be retrieved at any cost.
AI won't solve biology, make a pathogenic virus, etc, without tons and tons of data, of both types we know and types we have not yet figured out how to generate.
Perhaps the area where AI has the most to help bio is in figuring out novel measurement technology. But it's not going to be able to reason or deep-net its way to figuring out systems for which we can't even measure the parts.
Yep private scientific knowledge is a massive new moat that you can pull if you simply invest in scientific discovery methods in general and throw enough resources and tokens at the problem. I think there are already startups specifically trying to do this. The main obstacle is whether you're actually able to pull significantly ahead of (AI-enabled) public science to make a difference, but I guess the math works out if you're sufficiently AGI-pilled.
I agree this is a kind of data moat, but it's also arguably distinct enough to be its own thing.
I agree with this. I’ve seen it in my workplace but even more profoundly in my personal endeavors where I have more freedom to explore.
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...
The next crash will be from token costs increasing, if too many companies rely completely on the major AI companies.
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 think in-house AI developer tools (like the ones we’ve seen from Ramp, Stripe, DoorDash, etc.) are the beginning of this.
And on this, these are examples of tech companies filled with nerds who love novel new technology. Of course companies like Ramp and Stripe are spending huge amounts of time and money on taking this new technology to the extremes with “harnesses” and ”factories” because that’s what the nerds want to do. What the nerds want to do is not a sign of how the technology will be used in future, it’s a sign of what is most fun today.
It's not always what the nerds want to do though: I know of what the insides used to look like in one of those on the list, and things were very custom, because some early technical decisions were outright landmines that were too expensive to move away from. So having custom harnesses to actually survive that codebase I remember seems like a baseline to get reasonable turnaround of AI helpers. The company had done cowboy development for way too long, and solved too many problems by creating bigger, more custom problems.
So don't assume it's all nerds having fun. Now, is it what the future will look like for everyone? I don't think so, but that's because I expect AI is bringing us a more unified dev experience, with more developers than even Google has. This will make general programming harnesses very good, quite fast, solely because said harnesses can be differentiators in the AI race. So we are seeing some of the largest companies inthe world, with the largest research budgets, dedicating more money to the dev experience of their product than almost anyone else does.
> 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.
You're talking about the best businesses. Arguably the article is talking about the mean of the "startup" tranche of businesses.
Google isn't a SaaS. That's (is or is tangential to) the kind of businesses this article is referring to. And they make decent money, they just aren't a unicorn that didn't die.
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.
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.
Yeah 100% agree
My company can vibe code whole JIRA in two weeks even on a $200 plan and then they can stop paying the JIRA tax forever.
That's true for any product.
Who are they going to sell the product that this whole harness would produce? Any ideas?
We have evidence that this is not true. For years and years you have been able to self-host Zulip and Mattermost but companies still pay Slack millions of dollars per year. GitHub has billions in revenue, why not just use Gitea for free? So on and so forth. The ability to generate a (shitty) clone of software does not magically make the cloned software less valuable. Paying for software isn’t a “tax” when it produces value. If this vision of the future was true, cheap/free alternatives would have already destroyed the SaaS market years and years ago.
You'd agree that:
1. The quality of Mattermost and Zulip was NOT exactly as polished as Slack.
2. Deployment of such stacks was a problem. A complicate operational overhead.
These days, that's no more the case. Agents can:
1. Make pixel perfect clone of Slack or Jira with SQLite or ejabberd behind.
2. Or they can deploy the Zulip or Mattermost or GitLab for you just give them an SSH key to the machine and see them bringing the stack to life.
So now and back then are not the same.
I doubt that agents can make a product as polished as Slack. The status quo would indicate that's not the case as the prediction that SaaS would die has not panned out at all.
> The quality of Mattermost and Zulip was NOT exactly as polished as Slack.
Mattermost isn't actually all that bad and its mental model is pretty close to Slack, at least compared to Zulip. I actually prefer Mattermost to Teams, and would view it in the same ballpark as Slack.
> Deployment of such stacks was a problem. A complicate operational overhead.
Not at all! Their stack is actually very reasonable and quite easy to setup: https://github.com/mattermost/docker/blob/main/docker-compos...
Nowhere near the nightmare that self-hosting Sentry is like: https://github.com/getsentry/self-hosted/blob/master/docker-...
I'd say that in Mattermost's case the group calling functionality was locked behind a subscription as well as some other stuff last I checked, which makes it dead on arrival for many. It was quite nice software though, even before being able to vibe code your own (though tackling videos sharing and RTMP will be anything but trivial).
> Make pixel perfect clone of Slack or Jira with SQLite or ejabberd behind.
There's A LOT of functionality and features in Jira, the only thing that saves claims like that is that you probably don't use 80% and can just build what your company needs. I more or less did that for the hell of it (MariaDB + Garage + Dropwizard/Java + Angular/Spartan) and there was still so much supervision an changes that were needed - after pulling the Jira DB scheme and throwing about 60% of it in the trash (didn't need the automation), there was still A LOT of stuff to do and even the plans eat up a whole bunch of the context of the coding tools you intend to use, no matter how many checks and scripts you write to automate guardrails for when the models simply don't recall everything that is relevant.
For people who want to self-host something basic, there's already the excellent https://kanboard.org/ (very very fast)
For those that want something similarly depressingly slow to Jira while still locking some stuff behind a subscription (at least in the versions I tried) there's the passable https://www.openproject.org/
If those don't fit and you don't see anything exactly like what you need, you might try to build your own. But yeah, not a weekend project.
> Google isn’t successful because it has the best engineers, it has the best engineers because it is successful.
I think you missed the part where Google was successful because it had the best engineers, and then went all Alphabet.
There is such a thing as too big too fail.
> 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.
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 worth of tokens 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?
EDIT: Formatting
[0]. https://www.reddit.com/r/vibecoding/comments/1wpaies/my_vibe...
[1]. https://youtu.be/qOQ3MEx6czY
You're right but also very wrong.
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.
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.
> Harnesses orchestrate individuals
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.
If AI development is like management, harnesses will totally end up orchestrating people like traditional management does. The end state of the Jev type model hype is that a few years from now decision models replace management decision. Disconnected human managers five levels away from the work make suboptimal decisions all the time, the same logic applied to self driving cars can play out here. The model doesn't need to be perfect, it just needs to make better informed decisions than your fallible VP.
This is going to replace many layers of management who were pretty keen to use AI to replace their employees. So, poetic justice?
https://www.theguardian.com/technology/2026/sep/28/teachers-...
Also https://web.archive.org/web/20260725174355/https://marshallb...
Nice, wealthy scions cashing in on political connections and implementing the surveillance state.
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.
This explains the anticipation about Jev.
Trust is now what sells to the highest bidder.
I don't know about every SaaS. SaaS has evolved on it's own before LLMs every 3-5 years.
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.
this is rage bait whether intentional or not. Next time please think before you blog
thank you, I was confused why this is being taken so seriously by other commenters. If I wanted LinkedIn slop I would go to linkedin.com
The harness is the universe
The harness is the (physical) harness (on the team of oxen) (meta)
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?
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.
Judging by comment history, this new month old account of yours is just for dunking on AI development isn't it?
Software factories don't exist is an easily falsifiable statement https://github.com/topics/software-factory
yes very unserious software: https://abnormal.ai/
So abnormal is a harness? Website looks just like millions others
I had rented an office in the same building as them a few years back. Funny enough and unrelatedly, their VCs invited me for a meet at one point, so I researched their portcos and abnormal is one of them.
Their main thing, iirc, was detecting ~abnormal~ looking emails. It's the main way companies lose money and IP, it's not via crazy hacks, it's obviously the human element that is the biggest vulnerability in all companies. A stressed employee gets an email saying hey i'm the ceo i'm in a rush, or hey it's totally jennifer from your team i'm stuck outside, send me all the files/do x quickly. I don't have any fucking idea what they're doing now and I bet you they dont either
This must be a response to the cyber stuff from the big labs