Lately, I’ve been talking with quite a few software developers and people who run software companies, and there is one anxiety that keeps coming up.
AI can write product requirements. It can generate front-end interfaces, build back-end logic, and even create and run test cases.
Once the cost and difficulty of “writing code” itself are dramatically reduced by AI, what exactly will software developers and software companies be paid for?
I think the answer is becoming clearer.
When AI turns “building functionality” into a basic capability, the value of software will shift from whether something can be built to whether it can be built more cheaply, more smoothly, more reliably, and with greater control.
In the past, software companies competed on development efficiency and release speed.
In the future, the more important question may be this:
What additional value can you create on top of AI?
Here are four paths that I think are becoming increasingly important.
1. Become a Cost Optimizer
The first path is probably the most straightforward: help users spend less money and less time using AI.
For many users, the problem with large language models is not that they are incapable. The problem is that they can be expensive and inefficient.
Tokens cost money. Long reasoning chains cost time. Repeated prompt tuning, unnecessary context, and redundant model calls all create waste.
That waste creates an opportunity.
Instead of competing purely on price, software developers can build systems that make every model call more efficient.
For example, you could build prompt middleware that automatically removes irrelevant context, retrieves only the information that matters, and significantly reduces token consumption.
You could build smaller, task-specific models distilled from larger general-purpose models, allowing companies to run common workflows locally at much lower cost.
You could also optimize inference workflows by combining duplicate requests, caching repeated results, or skipping unnecessary reasoning steps so that a task that once required three model calls can be completed in one.
The business logic is simple:
If your software can reliably save users money, part of those savings becomes your pricing power.
In that sense, one emerging role for software companies is to become the “cost contractor” of the AI era.
2. Become a Scenario Builder
If the first path is about technical efficiency, the second is about understanding real-world workflows.
The opportunity is to package fragmented AI capabilities into a dedicated agent that solves a complete problem.
General-purpose AI has an obvious weakness: it can do almost anything, but users still have to figure out how to make it complete an actual business process.
They need to choose tools, write prompts, connect steps, verify outputs, and fix mistakes.
That is a lot of work.
The value of software is to hide that complexity.
A useful vertical agent can combine industry knowledge, multiple tools, business rules, and standardized workflows into a single product. The user does not need to understand prompting, model selection, or tool orchestration. They simply give the system a task and get a finished outcome.
For example, an e-commerce operations agent might handle product research, copywriting, image creation, listing preparation, and compliance checks as one continuous workflow.
A software delivery agent might turn a requirement into task breakdowns, code, unit tests, documentation, and code review.
The defensibility here is not the model itself.
The real moat is how well you understand the workflow, where human judgment is required, and how effectively you turn that knowledge into a repeatable process.
A general-purpose AI model is a Swiss Army knife.
A good vertical software product is a specialized machine built for one particular job.
3. Become the Compliance Gatekeeper
The faster AI spreads through companies, the more risk it creates.
That leads to a third path: build software that supervises AI rather than competes with it.
More companies are willing to use AI, but many are not willing to trust it completely.
Does AI-generated code contain security vulnerabilities?
Could it create open-source licensing or intellectual property risks?
Could generated content violate company policies or regulations?
Is confidential data being exposed?
Can the company reconstruct what happened when something goes wrong?
These questions become more important as AI moves from experimentation into production.
And they create a large category of software opportunities.
You might build tools that scan AI-generated code for security issues and licensing conflicts.
You might build content governance systems that automatically inspect generated text, images, or documents before publication.
You might build AI audit systems that record prompts, model calls, outputs, user modifications, and approval steps so that every important action is traceable.
In many enterprise environments, the product does not need to make the AI smarter.
It needs to make AI safe enough to use at scale.
As AI accelerates, someone still has to watch the traffic lights and apply the brakes.
That role will continue to have commercial value.
4. Build the Interface of Control
The fourth path may be even more important for professional users.
Many people do not actually want AI to “do everything for them.”
What they want is:
AI should help me work, but I should remain in control.
One reason people resist AI systems is that they often feel like black boxes.
The result appears, but the process is unclear.
If the result is wrong, users do not know where to intervene.
If they want to adjust one part, they may need to regenerate everything.
If they want to understand why the system produced a particular answer, the reasoning is often difficult to inspect.
This sense of losing control is a major problem in professional software.
That creates an opportunity for products where the user remains at the center of the workflow and AI operates quietly in the background.
For example, a visual AI generation tool could allow users to manually edit each intermediate step, lock specific elements, regenerate only selected sections, compare versions, and roll back changes.
A locally deployed AI platform could keep data inside the organization while allowing workflows, rules, and model behavior to be configured visually.
In products like these, the value is not simply “AI generation.”
The value is controlled AI generation.
Users should feel that the final result is still something they created and understood, with AI acting as an accelerator rather than an autonomous replacement.
That sense of control can itself become a strong reason to pay for software.
Software Development Is Not Disappearing. Its Value Is Moving.
AI is not eliminating software development.
It is changing what software development is valuable for.
In the past, companies competed on who could write code faster and ship more features.
In the future, the strongest software businesses may compete on something different:
Who can reduce AI costs?
Who can turn AI into a complete workflow?
Who can make AI safer and easier to govern?
Who can give users more control?
As implementation becomes cheaper, value moves closer to the user, the workflow, and the actual problem being solved.
These four paths are the clearest ones I see right now, but they are certainly not the only ones.
What other viable paths do you think software developers and software companies will have in the AI era?
I’d be interested to hear how others are thinking about it.
These notes reflect my thinking at the time of publication. Ideas may change as projects, evidence and experience change.