> 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 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.
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.
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.
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
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
> 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?
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.
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.
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.
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.
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
what must've happened is greylock told them "hey GPT Cyber, Cyber cyber cyber... do something!!!" and here we are
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.
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.
- 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)
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.
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.
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.
I agree this is a kind of data moat, but it's also arguably distinct enough to be its own thing.
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.
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
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
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...
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.
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
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 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...
Software factories don't exist is an easily falsifiable statement https://github.com/topics/software-factory
what must've happened is greylock told them "hey GPT Cyber, Cyber cyber cyber... do something!!!" and here we are