Knowing should lead to having, but the two are often at odds - what it means to be wealthy is roughly divided into corporate (knowing) wealth, extracted through payroll and contracts, and asset (having) wealth largely cashed out through institutions.
Knowers form consultancies, invest in startups, and ultimately sell knowledge to other knowers and a few clueless havers, but it’s only half the battle (knowing), the venturing enterprise must ultimately have what it takes
> If AI can build anything. Why do you need a human in the loop?
I'm currently building a SaaS with my business partner. I'm the "tech guy" while he has decades of industry experience with the industry that our SaaS is targeting.
What I found building this is there is a giant subset of preferences, problems, and other minor details that aren't written down anywhere, they are just kept in the minds of the people in that industry.
The reason it's not written down is to those people, these things seem obvious. And these things are both extremely important but also very subtle.
We use AI heavily in designing our application but there's always a testing pass where my partner goes over some changes and calls out subtle things of how "things work in practice". These things aren't in any manuals, they are seldom even written down and thus AI systems are completely oblivious to these details.
Now you could build a system that skips some of these subtleties and still gets the job done but what you will find is potential customers will always return to their existing system that takes these into account.
Now one day we might get an AI system that detects these subtleties but in order to do that you would need a load of training data around those things. But how do you even get that training data if all of it is held inside the minds of the people working in that industry?
I agree. I think people generally want a mix of good enough, easy, and something they already know.
The competition I see is from two separate directions, 1) AI companies training on the prompts you send them 2) users building their own a la carte solutions.
I don't think it entirely erases Saas, but it does take a huge bite out of it. Building a Saas product seems somewhat trivial at this point.
The larger hurdle now looks like having startup capital (free time to completely fail, or money to pay multiple founders to do that.) That's the risk, and the reward is income generated over time.
There also don't seem to be tons of SaaS paradigms at scale that differentiate companies other than entrenchment. It's somewhat realistic to assume that a handful of companies are going to pop up that connect simple applications to SaaS backends, which lowers even more the amount of income you can generate from a SaaS and would open up the level of competition to folks who can vibe code.
> Building a Saas product seems somewhat trivial at this point.
The trap here is the word "seems". When I started building my SaaS years back I thought the problem space was trivial (even joked to my business partner that I could spin the whole thing up in a weekend).
But almost 5 years on it's anything but trivial. The issue isn't with the core features, the core features in our app have been running for years without issue. What is very much non-trivial are all those subtle tiny features around the main feature that enhance the experience.
My customers don't pay me for the primary feature, that they could reproduce in a weekend. They pay me for all the tiny sub-features that came out of all those minor things that I mentioned in my first comment.
Those tiny details are the reason that it's both very easy to say things like "SaaS is trivial" and why the entrenched players are still at the top.
Interesting. You don't have to give away any secrets, but when you're talking about tiny details/sub features, what sorts of issues are you describing?
I know of a social org which runs of entirely on its people's face to face interaction. And, these folks have consulted recently with me about how they can adopt tech to facilitate their ops. I can sit with a LLM and build a platform for them, but idk it will cover the entirety of their day to day nuances.
You can't just throw resources at a road with muddy puddles everywhere and let a autonomous vehicle figure it out all by itself.
A perfect predictable problem is solvable with large resources, but the minute you touch something human, you need a equivalent human judgement to give it the sought after direction.
Honestly speaking, I think it was always possible for someone to replicate what you do in your SaaS, but maybe just a little slower. IMO the value was always in the fact that someone else is willing to obsess about the problem so you don't have to.
For example, I could probably always have make a Quizlet clone for myself, but even with agents around I'd rather just use Quizlet than spend the time making my own flashcard app, even if I have to pay like $5-10 a month. Just food for thought
I think it's less about this: You know about quizlet and choose to use it at $5/mo
And more about this: The average person doesn't know about quizlet and asks chatgpt to build them a flashcard app. They may pay $20/mo to have chatgpt build it.
Or this: If there's a blue ocean space where an app like quizlet doesn't exist, the competition is now 10x what it used to be. And the advertising cost is higher because there's more competition there.
2 kinds of value:
What you know
What you have
Knowing should lead to having, but the two are often at odds - what it means to be wealthy is roughly divided into corporate (knowing) wealth, extracted through payroll and contracts, and asset (having) wealth largely cashed out through institutions.
Knowers form consultancies, invest in startups, and ultimately sell knowledge to other knowers and a few clueless havers, but it’s only half the battle (knowing), the venturing enterprise must ultimately have what it takes
> If AI can build anything. Why do you need a human in the loop?
I'm currently building a SaaS with my business partner. I'm the "tech guy" while he has decades of industry experience with the industry that our SaaS is targeting.
What I found building this is there is a giant subset of preferences, problems, and other minor details that aren't written down anywhere, they are just kept in the minds of the people in that industry.
The reason it's not written down is to those people, these things seem obvious. And these things are both extremely important but also very subtle.
We use AI heavily in designing our application but there's always a testing pass where my partner goes over some changes and calls out subtle things of how "things work in practice". These things aren't in any manuals, they are seldom even written down and thus AI systems are completely oblivious to these details.
Now you could build a system that skips some of these subtleties and still gets the job done but what you will find is potential customers will always return to their existing system that takes these into account.
Now one day we might get an AI system that detects these subtleties but in order to do that you would need a load of training data around those things. But how do you even get that training data if all of it is held inside the minds of the people working in that industry?
I agree. I think people generally want a mix of good enough, easy, and something they already know.
The competition I see is from two separate directions, 1) AI companies training on the prompts you send them 2) users building their own a la carte solutions.
I don't think it entirely erases Saas, but it does take a huge bite out of it. Building a Saas product seems somewhat trivial at this point.
The larger hurdle now looks like having startup capital (free time to completely fail, or money to pay multiple founders to do that.) That's the risk, and the reward is income generated over time.
There also don't seem to be tons of SaaS paradigms at scale that differentiate companies other than entrenchment. It's somewhat realistic to assume that a handful of companies are going to pop up that connect simple applications to SaaS backends, which lowers even more the amount of income you can generate from a SaaS and would open up the level of competition to folks who can vibe code.
> Building a Saas product seems somewhat trivial at this point.
The trap here is the word "seems". When I started building my SaaS years back I thought the problem space was trivial (even joked to my business partner that I could spin the whole thing up in a weekend).
But almost 5 years on it's anything but trivial. The issue isn't with the core features, the core features in our app have been running for years without issue. What is very much non-trivial are all those subtle tiny features around the main feature that enhance the experience.
My customers don't pay me for the primary feature, that they could reproduce in a weekend. They pay me for all the tiny sub-features that came out of all those minor things that I mentioned in my first comment.
Those tiny details are the reason that it's both very easy to say things like "SaaS is trivial" and why the entrenched players are still at the top.
Interesting. You don't have to give away any secrets, but when you're talking about tiny details/sub features, what sorts of issues are you describing?
If your startup goes successful. Anthropic will copy your model and make it there’s. They are distilling each and every knowledge
For me, the goal is to see what happens when you persist and carve out your own market share.
Despite the abundance of tools available today, there's always room to deliver unique value to users (e.g., https://docuqueue.com/#mcp )
Playing the long game just takes patience and composure.
I know of a social org which runs of entirely on its people's face to face interaction. And, these folks have consulted recently with me about how they can adopt tech to facilitate their ops. I can sit with a LLM and build a platform for them, but idk it will cover the entirety of their day to day nuances.
You can't just throw resources at a road with muddy puddles everywhere and let a autonomous vehicle figure it out all by itself.
A perfect predictable problem is solvable with large resources, but the minute you touch something human, you need a equivalent human judgement to give it the sought after direction.
AI can build anything... within the tiny slice of the economy/human experience that is data/software.
It turns out there's a vast universe out there involving physical products, real estate, agriculture, plumbing, sanitation, health care...
That tiny slice is gigantic.
Honestly speaking, I think it was always possible for someone to replicate what you do in your SaaS, but maybe just a little slower. IMO the value was always in the fact that someone else is willing to obsess about the problem so you don't have to.
For example, I could probably always have make a Quizlet clone for myself, but even with agents around I'd rather just use Quizlet than spend the time making my own flashcard app, even if I have to pay like $5-10 a month. Just food for thought
I understand what you're saying.
I think it's less about this: You know about quizlet and choose to use it at $5/mo
And more about this: The average person doesn't know about quizlet and asks chatgpt to build them a flashcard app. They may pay $20/mo to have chatgpt build it.
Or this: If there's a blue ocean space where an app like quizlet doesn't exist, the competition is now 10x what it used to be. And the advertising cost is higher because there's more competition there.
AI can't build everything, that assumption is just wrong. Do you even code?
Yes. The point isn't whether it's true. It's currently the reality that's being sold by most large AI firms.
Prediction: There will be new jobs.
My advice is to find these types of jobs, like powering data centers, cleaning up vibe coded messy codebases as a consultant.
I should start a power company?
Maybe you should!
The world needs more jobs and companies that are not just yet another SaaS software or AI startup.