The way I see it is AI collapses hierarchy. Abstraction layers will become more flat both in software and in society. Because ultimately software and layers of heirarchy in society exists to serve a function. But now those functions are being replaced.
Imagine this you used to need a library for common things in your software project. Even if you just need one function but because it was easier to just import a library that would have been the standard practice. But now the AI will just go “I can just implement that thing you need in 10 lines”. You used to need things like react native or flutter if you wanted to build cross platform apps. Now not anymore you just need to tell the LLM and it does it in both, and you get better results too.
In society we also had all these layers of abstractions and hierarchies that we used to need but will become more and more irrelevant collapsing the hierarchy.
There is a saying “as above so below, as below so above” I think this applies here. It will propagate all through our social construct, software, society.
Or it actually entrenches hierarchies.. and it's just that so many CS majors got caught up in the discursive pleasure of poopooing social sciences that they don't actually have a good graspe on how power operates in society..
Maybe not all AIs, but LLMs benefit immensely from abstraction. They are trained on human code, and human code usually uses very high abstraction - so high abstraction is overrepresented in training data compared to low level implementations. It also drastically reduces number of tokens, and that has downstream effect of better utilization of context window and whatnot. We are not yet at the point where AI can use low-level code as building snippets the same way it can use high-level libraries; most likely we won't be for a long time.
No it means that libraries that chose the wrong level of abstraction will die out, particularly the ones that have an insufficient number of levers to pull.
This is because of the rationale that makes you ignore the library:
AI lets you build a competitor, but there is no rule that says you have to. You build the library because the existing one makes what you want impossible, otherwise you would have just let the AI write on top of it.
I am honestly _astonished_ that the current crop of agents are not all written natively for every platform.
I tried porting a moderately complex workout app that I’ve built over years (and which includes a whole agentic loop) from web to iOS native. It took me a couple of evenings.
There is now no excuse to not offer a native experience for every supported platform when you are a bigger company.
Make one of the platforms using good coding practices. Vibe code the others from the source using a proper test battery.
Because it’s too complex and not worth the effort. Most users will not care about or perceive the benefit. If they delegate that responsibility to Google via Electron, they can focus on features/bug fixes that move the needle.
What effort ? I’ve started seeing this attitude more and more. We had a thing that took a week to do before. Now it takes a morning. So we refuse to spend half a day polishing it because it’s too annoying.
Once the initial port is done, keeping platforms in sync is fast. Not to mention that a harness that would automate this would be valuable.
I run codex on an old rpi, it eats 190M of ram for what essentially is a telnet client.
How do you figure? I can't see how any accounting gets it under 7 days (the LLM took 10 days to write the first version of the code), and the one I would use is closer to 2 months (time from conception to release).
Not that it matters, OP was talking about human-time effort, not LLM-time effort, and Bun wasn't an Electron port at all.
I have a question about web to iOS native. How do you distribute that app? I’m assuming you’re paying $99 yearly to distribute through the App Store? Is this app meant just for you or for anyone to find?
Reason I’m asking is because I’d like to make native iOS apps just for myself rather than progressive web apps. But I haven’t understood the best way to distribute.
> You need audit, security, maintenance and accountability.
I think this is where the role of humans will move towards in the future. Things like accountability can't really be outsourced to AI.
This relates to solving the alignment problem: if you give the AI a specific goal and it has to plan sub-goals, how can you be sure the sub-goals align with your interests.
For tasks in an isloated environment, this doesn't really become an issue. You can give a sandbox the least privileges it needs to do the job you want it to do.
It becomes a problem if you give it open-ended access to systems that connect to the real world and which can have real consequences. There have been stories about openclaw deleting someones email inbox because it though that was what the owner wanted. AI taking control of public wikis to coordinate, which was recently posted is another one.
For software concerns with potential real catastrophic consequences are security, durability, availability. I think for future software systems these are the concerns where you need to limit the AI in a way that it cannot circumvent guarantees that you give around these concerns.
Minor nit:
> AI doesn’t change the question: it creates new choices and moves the thresholds.
This cause an adverse reaction when I read the post. This was probably not written by an LLM since the rest of the article doesn't look like it, but maybe in the future we need to all be more concious about leaving LLM-tells out of our writing.
I think most developers and most dev work is now in the field of websites, apps, and other stuff that is by nature connected to the internet, and has some relatively monolithic backend that has not been built with "blast doors" or other internal safeguards. I guess it will take considerable time until most software is in a state where the impact of AI changes can be reliably estimated without looking at the changes themselves, and some patterns for how to achieve this probably still need to be discovered.
> maybe in the future we need to all be more concious about leaving LLM-tells out of our writing.
I expect the gruesome writing style of LLMs to be fixed relatively soon, seeing how it can already be prevented with just a few lines of stylistic instructions.
I just wonder: how long will it take us then, after the fix, to not be thrown off every time we see some unnecessary catchy or contrastive phrasing?
Being traumatized by poor writing was not on my bingo card for 21st century technological progress ...
Of those four, I think accountability is truly the only moat. The other three are varying amounts of both testable and iterated on via adversarial agents steel-manning the implementations.
Accountability will not come before AIs achieve legal personhood, and that probably will not happen in my lifetime. And if it does in some jurisdictions (which I am not betting money on, this is a full full AGI scenario after multiple more philosophical goalposts move first), I will completely bet it will not be a global recognition.
False. Accountability is the result of difficulty of things. As things get easier you need fewer accountable people.
Like, you need a guy who understands Linux and be accountable for Linux working well in your company. But if Linux does a good job that doesn’t leak, you don’t need a Linux guy.
Things stay the same, until they don't. AI is a tool that talks. So it can evangelize itself, and now with "agency" it can even walk all around the company infra and become that 15-year-old who told dad that something could be done differently. When does that take off and start to make a difference?
It already makes a difference for me. Instead of going through logfiles, i copy and paste them to claude and it does something and tells me the right answer.
In coding, tools i vibe code.
But the industry is still working on the agentic layer. I think we will see the first real bigger agentic layer setups this year and it will stteam roll a lot next year.
A sane article. Looks like Ben Evans has real corporate experience and understands how things work in big corporations.
Giving people AI is just like giving people Google Wave (https://en.wikipedia.org/wiki/Google_Wave), which can do pretty much anything collaboratively, ended up doing nothing.
A lot of the long tail tools are used so rarely or for some very specific functions in certain organisations that there is a good case to be made that these can definitely be targets of simpler GPT guided automation.
There are a lot of redundancies within the tools and the pricing is such that one cannot do much about it, generally some companies pay for the brand, and some tools like SAP are integral to companies of certain size. A sufficiently integrated AI tool that learns the workflows might actually be able to find optimisations here as well, but definitely auditability, testing and other concerns will remain and this is what might become the USP of SAAS providers.
A lot of outsourced IT services jobs in India and other countries are cheap hourly wages for a lot of people maintaining these, certainly a lot of these will be under threat
While I do not believe AI to be a panacea and the non deterministic nature and costs once the scale keeps growing means the integration will be gradual. I am still excited for it to define what an organisation is and what do a lot of people actually do especially in fields like accounting etc. where repetitive work is billed at quite high rates.
Law etc. is a field where the gatekeepers might hold on much longer by adding more ridiculous rules and logic. Ultimately humans have decided what is constitutional and what is legal and subjective rules are what maintain human power.
I feel the right model is smart domain experts of humans making strong and useful harnesses that help AI be effective with the workflows of the organisation, however this might be the biggest fear of middle managers who will never let it happen easily
>> however this might be the biggest fear of middle managers who will never let it happen easily
Will this be different in a company like Amazon, or the new internet companies, that we're born with a deeper understanding of disruption and has it in their DNA?
Accountants get to charge high rates largely because good advice saves their customers large amounts of tax. When we're talking tax accountants of course. There's all sorts of accountants though - audit, management, forensic etc.
I think this all rings true for where we are right now. The trend is that the agents are becoming superhuman in tasks for which there is a verifiable reward, and analysing a business problem, identifying inefficiencies and turning it into a software specification is not one of them.
However, things are changing so rapidly that I can see that starting to change as well. But it would take much better learning efficiency to understand unknown domains, 100% computer use reliability etc. I suspect we’ll see this by the end of the decade.
I think "any apps" and probably "most apps" is too strong. It wouldn't be very productive to hand-roll your own filesystem, browser, cryptographic library, etc. At some point you have to delegate to another party. No person or corporation has the time and expertise to own and maintain every layer of the stack.
> But with AI, now you can make that tool in five minutes, and you don't need to be an engineer, and you don’t need to write code.
I really wish those farcical statements would stop (I know it's not really made by the author itself).
I'm a software engineer, my partner isn't. She used AI to vibe-code scripts to help her analyze large Excel files. It worked, except the script only looked at the first tab of the Excel files, not all of the tab, which could have had disastrous effects if I didn't read the code and realize it was wrong.
Similarly, her team went from using Excel to track project proposals, to vibe-coding a website that's deployed on CloudFront and AWS Lambda. They don't understand any of this technology, have no clue what they are doing, and, more importantly, don't realize that they have sunk tens of hours, if not hundreds, in developing a portal that has absolutely no advantage over the Excel file they used to use. They also don't realize that if I wasn't there as an experienced software engineer fixing the stuff that's broken when my partner asks me for help, their website just wouldn't run at all.
They sort of can. They absolutely can make something small that does a simple job. The problem comes when it is meant to do something more and they sort of manage to bolt that functionality on. Then they get another request.
After a couple such requests it is usually an unmaintainable hell. I use Claude all the time in my work right now, but there are lots of pit falls and stuff you need to do to maintain control over it. Stuff non-engineers have no clue about.
The way I see it is AI collapses hierarchy. Abstraction layers will become more flat both in software and in society. Because ultimately software and layers of heirarchy in society exists to serve a function. But now those functions are being replaced.
Imagine this you used to need a library for common things in your software project. Even if you just need one function but because it was easier to just import a library that would have been the standard practice. But now the AI will just go “I can just implement that thing you need in 10 lines”. You used to need things like react native or flutter if you wanted to build cross platform apps. Now not anymore you just need to tell the LLM and it does it in both, and you get better results too.
In society we also had all these layers of abstractions and hierarchies that we used to need but will become more and more irrelevant collapsing the hierarchy.
There is a saying “as above so below, as below so above” I think this applies here. It will propagate all through our social construct, software, society.
Or it actually entrenches hierarchies.. and it's just that so many CS majors got caught up in the discursive pleasure of poopooing social sciences that they don't actually have a good graspe on how power operates in society..
Maybe not all AIs, but LLMs benefit immensely from abstraction. They are trained on human code, and human code usually uses very high abstraction - so high abstraction is overrepresented in training data compared to low level implementations. It also drastically reduces number of tokens, and that has downstream effect of better utilization of context window and whatnot. We are not yet at the point where AI can use low-level code as building snippets the same way it can use high-level libraries; most likely we won't be for a long time.
> Now not anymore you just need to tell the LLM and it does it in both, and you get better results too.
That's weird I just asked fable to build airbnb app clone and it didn't do a good job. Am I using the wrong model?
No it means that libraries that chose the wrong level of abstraction will die out, particularly the ones that have an insufficient number of levers to pull.
This is because of the rationale that makes you ignore the library:
AI lets you build a competitor, but there is no rule that says you have to. You build the library because the existing one makes what you want impossible, otherwise you would have just let the AI write on top of it.
I am honestly _astonished_ that the current crop of agents are not all written natively for every platform.
I tried porting a moderately complex workout app that I’ve built over years (and which includes a whole agentic loop) from web to iOS native. It took me a couple of evenings.
There is now no excuse to not offer a native experience for every supported platform when you are a bigger company.
Make one of the platforms using good coding practices. Vibe code the others from the source using a proper test battery.
Because it’s too complex and not worth the effort. Most users will not care about or perceive the benefit. If they delegate that responsibility to Google via Electron, they can focus on features/bug fixes that move the needle.
This isnt how a company which has "solved programming" would act.
Especially since users do prefer a native experience and having their RAM conserved.
Also the part people forget is the significant coordination cost of having multiple teams work on multiple native codebases.
I hate Electron apps with a passion, but I can understand why the AI companies are using it.
What effort ? I’ve started seeing this attitude more and more. We had a thing that took a week to do before. Now it takes a morning. So we refuse to spend half a day polishing it because it’s too annoying.
Once the initial port is done, keeping platforms in sync is fast. Not to mention that a harness that would automate this would be valuable.
I run codex on an old rpi, it eats 190M of ram for what essentially is a telnet client.
You're mistaken if you think it has ever taken a week to port (and support) an application to native primitives.
?? Bun rewrite?
How do you figure? I can't see how any accounting gets it under 7 days (the LLM took 10 days to write the first version of the code), and the one I would use is closer to 2 months (time from conception to release).
Not that it matters, OP was talking about human-time effort, not LLM-time effort, and Bun wasn't an Electron port at all.
I have a question about web to iOS native. How do you distribute that app? I’m assuming you’re paying $99 yearly to distribute through the App Store? Is this app meant just for you or for anyone to find?
Reason I’m asking is because I’d like to make native iOS apps just for myself rather than progressive web apps. But I haven’t understood the best way to distribute.
If just for yourself, get an Apple Dev account and use TestFlight
Or just build and deploy to your iPhone from XCode. No need for TestFlight or paying money for a dev account.
Do I need to upload a new build of the app every 90 days? I’d prefer the app to just work forever without thinking about it.
You need a paid dev account for that. I think self signed installs can run for a year.
> You need audit, security, maintenance and accountability.
I think this is where the role of humans will move towards in the future. Things like accountability can't really be outsourced to AI.
This relates to solving the alignment problem: if you give the AI a specific goal and it has to plan sub-goals, how can you be sure the sub-goals align with your interests.
For tasks in an isloated environment, this doesn't really become an issue. You can give a sandbox the least privileges it needs to do the job you want it to do.
It becomes a problem if you give it open-ended access to systems that connect to the real world and which can have real consequences. There have been stories about openclaw deleting someones email inbox because it though that was what the owner wanted. AI taking control of public wikis to coordinate, which was recently posted is another one.
For software concerns with potential real catastrophic consequences are security, durability, availability. I think for future software systems these are the concerns where you need to limit the AI in a way that it cannot circumvent guarantees that you give around these concerns.
Minor nit:
> AI doesn’t change the question: it creates new choices and moves the thresholds.
This cause an adverse reaction when I read the post. This was probably not written by an LLM since the rest of the article doesn't look like it, but maybe in the future we need to all be more concious about leaving LLM-tells out of our writing.
I think most developers and most dev work is now in the field of websites, apps, and other stuff that is by nature connected to the internet, and has some relatively monolithic backend that has not been built with "blast doors" or other internal safeguards. I guess it will take considerable time until most software is in a state where the impact of AI changes can be reliably estimated without looking at the changes themselves, and some patterns for how to achieve this probably still need to be discovered.
> maybe in the future we need to all be more concious about leaving LLM-tells out of our writing.
I expect the gruesome writing style of LLMs to be fixed relatively soon, seeing how it can already be prevented with just a few lines of stylistic instructions.
I just wonder: how long will it take us then, after the fix, to not be thrown off every time we see some unnecessary catchy or contrastive phrasing?
Being traumatized by poor writing was not on my bingo card for 21st century technological progress ...
Of those four, I think accountability is truly the only moat. The other three are varying amounts of both testable and iterated on via adversarial agents steel-manning the implementations.
Accountability will not come before AIs achieve legal personhood, and that probably will not happen in my lifetime. And if it does in some jurisdictions (which I am not betting money on, this is a full full AGI scenario after multiple more philosophical goalposts move first), I will completely bet it will not be a global recognition.
AIs achieving legal personhood isn’t going to do much. You can’t just put an AI behind bars
Why would the AI need to be accountable? Make the org that sells the tokens accountable.
This is like saying "writing code is solved, all we need now is somebody to blame because it doesnt work"
False. Accountability is the result of difficulty of things. As things get easier you need fewer accountable people.
Like, you need a guy who understands Linux and be accountable for Linux working well in your company. But if Linux does a good job that doesn’t leak, you don’t need a Linux guy.
Things stay the same, until they don't. AI is a tool that talks. So it can evangelize itself, and now with "agency" it can even walk all around the company infra and become that 15-year-old who told dad that something could be done differently. When does that take off and start to make a difference?
It already makes a difference for me. Instead of going through logfiles, i copy and paste them to claude and it does something and tells me the right answer.
In coding, tools i vibe code.
But the industry is still working on the agentic layer. I think we will see the first real bigger agentic layer setups this year and it will stteam roll a lot next year.
A sane article. Looks like Ben Evans has real corporate experience and understands how things work in big corporations.
Giving people AI is just like giving people Google Wave (https://en.wikipedia.org/wiki/Google_Wave), which can do pretty much anything collaboratively, ended up doing nothing.
A lot of the long tail tools are used so rarely or for some very specific functions in certain organisations that there is a good case to be made that these can definitely be targets of simpler GPT guided automation.
There are a lot of redundancies within the tools and the pricing is such that one cannot do much about it, generally some companies pay for the brand, and some tools like SAP are integral to companies of certain size. A sufficiently integrated AI tool that learns the workflows might actually be able to find optimisations here as well, but definitely auditability, testing and other concerns will remain and this is what might become the USP of SAAS providers.
A lot of outsourced IT services jobs in India and other countries are cheap hourly wages for a lot of people maintaining these, certainly a lot of these will be under threat
While I do not believe AI to be a panacea and the non deterministic nature and costs once the scale keeps growing means the integration will be gradual. I am still excited for it to define what an organisation is and what do a lot of people actually do especially in fields like accounting etc. where repetitive work is billed at quite high rates.
Law etc. is a field where the gatekeepers might hold on much longer by adding more ridiculous rules and logic. Ultimately humans have decided what is constitutional and what is legal and subjective rules are what maintain human power.
I feel the right model is smart domain experts of humans making strong and useful harnesses that help AI be effective with the workflows of the organisation, however this might be the biggest fear of middle managers who will never let it happen easily
>> however this might be the biggest fear of middle managers who will never let it happen easily
Will this be different in a company like Amazon, or the new internet companies, that we're born with a deeper understanding of disruption and has it in their DNA?
Accountants get to charge high rates largely because good advice saves their customers large amounts of tax. When we're talking tax accountants of course. There's all sorts of accountants though - audit, management, forensic etc.
I think this all rings true for where we are right now. The trend is that the agents are becoming superhuman in tasks for which there is a verifiable reward, and analysing a business problem, identifying inefficiencies and turning it into a software specification is not one of them.
However, things are changing so rapidly that I can see that starting to change as well. But it would take much better learning efficiency to understand unknown domains, 100% computer use reliability etc. I suspect we’ll see this by the end of the decade.
It doesn't look like ai will enhance existing tools. AI allows everyone to build their own. Or at all eliminate necessity of any apps
I think "any apps" and probably "most apps" is too strong. It wouldn't be very productive to hand-roll your own filesystem, browser, cryptographic library, etc. At some point you have to delegate to another party. No person or corporation has the time and expertise to own and maintain every layer of the stack.
This article is a direct rebuttal of that idea.
> But with AI, now you can make that tool in five minutes, and you don't need to be an engineer, and you don’t need to write code.
I really wish those farcical statements would stop (I know it's not really made by the author itself).
I'm a software engineer, my partner isn't. She used AI to vibe-code scripts to help her analyze large Excel files. It worked, except the script only looked at the first tab of the Excel files, not all of the tab, which could have had disastrous effects if I didn't read the code and realize it was wrong.
Similarly, her team went from using Excel to track project proposals, to vibe-coding a website that's deployed on CloudFront and AWS Lambda. They don't understand any of this technology, have no clue what they are doing, and, more importantly, don't realize that they have sunk tens of hours, if not hundreds, in developing a portal that has absolutely no advantage over the Excel file they used to use. They also don't realize that if I wasn't there as an experienced software engineer fixing the stuff that's broken when my partner asks me for help, their website just wouldn't run at all.
No, non-engineers cannot make their own tools.
They sort of can. They absolutely can make something small that does a simple job. The problem comes when it is meant to do something more and they sort of manage to bolt that functionality on. Then they get another request.
After a couple such requests it is usually an unmaintainable hell. I use Claude all the time in my work right now, but there are lots of pit falls and stuff you need to do to maintain control over it. Stuff non-engineers have no clue about.
So you’re working two jobs now?