Vladimir Rezin
There is a phrase that sounds almost like a joke:
AI for every household.
Remove the irony, however, and it points to one of the most uncomfortable shifts the software industry may be about to face.
For decades, software was an expensive artifact. Automating even a relatively simple task required a team. Someone had to define the requirements, write the code, build the interface, deploy the environment, maintain the system, fix bugs, secure the data, manage integrations, and keep the entire thing operational.
That economic reality gave rise to SaaS.
One product for many customers. One standardized workflow. One admin panel. One subscription. One roadmap attempting to serve hundreds or thousands of users whose needs were similar – but never identical.
It was a rational model. Custom software was too expensive, so SaaS spread the cost of development across many customers.
But what happens when the cost of creating a custom solution falls dramatically?
How AI agents turn code into a disposable tool
Recently, I was reviewing my personal finances. I had years of income and expense history stored in an app, and I wanted to calculate how much I had spent renovating an apartment. The problem was that not every transaction had been assigned to the correct category.
This was not a simple “show me all renovation expenses” query.
The task required going through the data, identifying similar payments, accounting for classification errors, testing several assumptions, and assembling a reliable result.
I asked an AI agent to do it. The agent analyzed the data. But what mattered more was how it got there.
In the process, it wrote more than ten scripts. It debugged them, rewrote some of them several times, added checks, ran tests, corrected errors, and eventually produced the result.
Those scripts were not a product.
I had no intention of maintaining them for years. I was not going to sell them or turn them into a service. They were temporary tools created to produce one specific outcome. I used them and discarded them.
That, in my view, is more important than the financial analysis itself. An AI agent can now create and destroy software on demand. It is not merely helping a developer write code. It is using code as a consumable resource.
This changes the role software can play. Not every piece of code needs to become a maintained application, a platform, or a subscription product. Some software can exist for an hour, solve one problem, and disappear.
Will AI agents replace SaaS – or only standardized SaaS?
SaaS is not going to disappear tomorrow.
Payment systems, accounting platforms, CRMs, communication tools, legally binding document workflows, infrastructure platforms, security tools, and enterprise systems with years of accumulated data and integrations are not going anywhere quickly.
But a large category of standardized software may come under pressure.
These are products that exist largely because creating a small custom solution for an individual, a family, a founder, or a small business used to be prohibitively expensive.
When customers buy SaaS, they are effectively accepting a compromise. The product solves a standard problem shared by people like them, not their exact problem. They must adapt to the product’s interface, fit their data into someone else’s model, and fit their process into someone else’s workflow. They pay for a large feature set while regularly using only a small part of it. If they need something different, they wait for a roadmap update, an integration, a plugin, or another export-import cycle.
Until recently, this compromise was almost unavoidable.
Now users can begin asking a different question: Why should I pay for an average workflow if an agent can assemble a solution around my specific task?
That proposition is not always realistic yet. Reliability, security, integrations, and operating costs still matter.
But the direction is already visible.
The real threat is not that every established SaaS platform will be replaced by an AI-generated clone. The threat is that customers may no longer accept the standardized workflow as the default interface between their problem and its solution.
Custom software without becoming a programmer
When people say that “every household will be able to write its own Python script,” they do not mean that everyone will become a programmer.
The opposite is more likely. People will no longer need to become programmers.
They will describe the outcome they need in natural language: find every renovation expense, even if the categories are inconsistent; collect the documents needed for their taxes; check whom they promised to get back to; identify customers who have no next step assigned; compare the terms offered by suppliers; create a small form for collecting requests; export, clean, and validate data before producing a conclusion; build a personal knowledge base from their documents; or create a small tool that solves a specific operational problem.
In a property management business, the same pattern could apply to maintenance requests, vendor records, inspection documents, or unresolved tenant communications. The user would describe the desired result rather than search for another application with the right combination of screens and filters.
The agent would not merely answer a question.
It would access data, write code, call APIs, create temporary tools, validate the output, save artifacts, send notifications, and update tasks.
To the user, this would not look like software development. It would look like a personal operating environment doing its job.
How AI agents are changing software development
As code becomes cheaper, the market value of simply translating clear requirements into standard CRUD functionality begins to decline.
This is not because the people who perform that work are bad engineers.
The market has historically paid for a scarce capability: taking a defined requirement and turning it into a functioning interface, form, table, admin panel, report, or integration.
AI reduces the scarcity of that capability.
When an agent can generate in an hour the amount of code a person might previously have written in a day, the constraint moves.
This is an uncomfortable shift.
If a person has to manually inspect every line of code generated by an agent, the process will quickly reach a limit. The volume of generated code will grow faster than a person’s ability to read and understand it properly.
Part of the control process will therefore have to be delegated to automated tests and validation, logs and audit trails, sandbox environments, rollback mechanisms, restricted execution permissions, human approval at the right boundaries, and explicit responsibility boundaries.
The value of an engineer changes as a result.
Less: I write all the code myself.
More: I design an environment in which an agent can safely produce a result.
This is not the disappearance of engineering. It is a change in what the most valuable engineering work looks like.
Building with AI requires more than faster code.
Explore how AI-assisted development can help you create adaptive workflows and custom tools without compromising security, reliability, or control.
We’ll review your use case and suggest practical next steps within one business day.
Responsibility does not disappear
The most uncomfortable question in this model is simple: Who is responsible?
If one agent writes the code, another checks it, a third deploys it, and the user receives a bad result, who is accountable?
“AI did it” is not an answer a business can use.
In practice, responsibility will still land somewhere: with the process owner, the solution provider, the person who started the agent, the company that accepted the risk, or the governance system that authorized the change.
Responsibility therefore becomes more than a philosophical question. It becomes a product capability.
The system needs clear boundaries. It must define what the agent can do independently, where human approval is required, what qualifies as a verified result, what is logged, what can be rolled back, which actions are prohibited, who accepts the risk, and where the evidence explaining a decision is stored.
This is especially important in regulated or operationally sensitive environments. An agent that drafts a report is one thing. An agent that changes a rent ledger, sends a legally significant notice, modifies access permissions, or triggers a payment is something else entirely.
“AI for every household” does not mean that everyone will generate software chaotically without controls.
Following the same logic leads to a different product layer.
Beyond SaaS: a private operating environment
A strong alternative to SaaS may not look like another chatbot.
It may look like a private, protected operating environment built around an individual, a family, a founder, or a small business.
This environment brings together data, documents, tasks, calendars, email, finances, projects, workflows, permissions, models, tools, logs, rules, and automations. It also provides the ability to create small pieces of software for specific tasks.
AI plays several roles inside this environment.
The first role is orchestrator.
It connects tools, routes tasks, invokes the right models, and understands where the relevant data and documents live. It knows when to use an external service, when to involve a person, and when to request permission.
The second role is validator, assistant, and secretary.
It checks results, extracts decisions, tracks commitments, maintains context, prepares summaries, highlights risks, and ensures that important information does not remain buried in a chat.
The third role is software developer.
It creates scripts, small applications, integrations, forms, reports, and temporary tools for specific tasks.
This leads to an important product shift.
The customer is no longer buying only an application.
The customer is buying the ability of a private operating environment to create the tools it needs, validate them, use them, and discard them.
The distinction matters. A conventional SaaS product sells access to a predefined set of capabilities. A private operating environment sells the capacity to assemble new capabilities around a user’s context.
What will still create value in enterprise software?
If generation speed becomes available to everyone, speed itself will stop being a meaningful advantage.
Value will move to the areas where an agent operating alone remains weak: understanding the domain model, prioritizing problems by importance, accepting responsibility, maintaining security, working with accumulated data, integrating with the real world, validating and supporting results, explaining why the system acted in a particular way, and preventing rapid development from becoming a rapid Big Ball of Mud.
This is particularly important in enterprise software.
Completely regenerating a system sounds attractive until that system contains years of data, dozens of integrations, thousands of users, internal policies, contracts, reporting requirements, and dependent business processes.
In the real world, the main challenge is often not writing new code.
It is avoiding damage to the world that already exists.
For a PropTech company, that world may include payment records, leases, maintenance histories, accounting integrations, tenant communications, vendor workflows, and local compliance requirements. A newly generated interface may be cheap. Preserving the meaning and integrity of the underlying operations is not.
If SaaS does decline, it will not mean the death of every platform.
It will be the end of the standardized workflow’s monopoly.
Where people once had to adapt to a product, the product will increasingly adapt to the person or business using it.
What the shift means for SaaS companies
Software companies have traditionally treated the codebase as one of their primary assets.
That will no longer be enough.
The real asset will be the company’s ability to understand the customer’s actual pain quickly, formalize the domain, build a secure operating environment, give agents the right tools, validate and explain the result, support it over time, and reduce risk rather than only the cost of development.
For SaaS companies, this creates a strategic choice.
They can continue treating the standardized workflow as the product, adding more features to accommodate more variations. Or they can begin treating their data models, permissions, integrations, governance, and domain expertise as a foundation on which more adaptive workflows can be created.
The defensible layer may move away from the interface itself.
And that may be good news.
The penetration of software into everyday life and small business remains surprisingly limited. Not because people do not need better tools, but because traditional software development has been too expensive and cumbersome for small, highly specific problems.
As the cost of creating solutions falls, a market becomes accessible that traditional software could rarely reach. It includes personal operating environments, AI offices for founders, private environments for family finances and documents, small automations for microbusinesses, CRM and task workflows without a heavy implementation project, and private AI assistants that do not merely answer questions but manage work.
These needs existed before AI. In many cases, they simply were not economically viable as software products.
The future of SaaS is adaptive, not standardized
SaaS succeeded because custom software was too expensive.
AI agents are beginning to change that equation.
As custom solutions become cheaper, users will increasingly ask an uncomfortable question:
Why should I adapt to an average product when an operating environment can be assembled around the reality of my life or business?
The answer is not that all SaaS products will disappear.
The answer is that value will move away from the standardized application and toward private context, domain models, automation, validation, responsibility, and the ability to create the right tools quickly.
“AI for every household” is not a prediction that everyone will become a programmer.
It is a prediction that programming will no longer be the primary way people obtain small software solutions.
The next major competition in software may therefore not be for another SaaS category.
It may be for the right to become the private operating environment of a person or a business.
✍️ Setronica helps companies modernize existing platforms, integrate AI into real workflows, and build custom software around their data, domain, and operational requirements. Talk to our engineering team about where AI can create real value in your product – and where reliability, governance, and human control still matter.
