Why AI is changing the value of software, and why entrepreneurs should spend less time improving yesterday’s solutions and more time understanding tomorrow’s problems.
There is a big discussion going on about the future value of software in a world increasingly defined by artificial intelligence.
If everybody can create software, what happens to the companies that have spent years developing and selling it?
If AI can build an application in hours that previously required a team of developers working for months, why should customers continue paying the same prices?
These are reasonable questions, and I believe the software industry should take them seriously.
But I also believe that much of this discussion starts from the wrong point.
We are discussing the future value of software by looking at the problems of the past.
And that is exactly where I think the biggest opportunity for entrepreneurs is being overlooked.
The Value Was Never in the Software Itself
I have been working in software and technology for more than 25 years, and I have experienced several important technological changes as an entrepreneur.
Every significant change brought new tools, new competitors and new business models. Technologies that were expensive and difficult to develop became accessible to more companies, and products that once represented a competitive advantage gradually became standard functionality.
AI is accelerating this process dramatically.
For many years, software companies created value by developing tools that solved well-understood business problems. The development itself required significant investment, specialized knowledge and time. These barriers protected established software businesses and allowed them to charge for their products.
But what happens when those barriers become much smaller?
A tool that once required a large development team can increasingly be created by a much smaller team using AI. Features that previously justified a separate product can become standard capabilities available to almost everybody.
The functionality may remain useful, but its commercial value can decline.
This distinction is important.
A problem can remain important even when the software solving it becomes a commodity.
I do not believe that AI will make all existing software worthless. Reliable operations, security, integrations, compliance and customer trust will continue to matter. But I do believe that the competitive advantage of simply having software that solves an already well-understood problem will become much weaker.
And many software companies are still building their strategies around precisely that advantage.
We Are Looking at the Wrong Side of the Equation
Imagine an entrepreneur who wants to start a software company today.
The traditional approach would be to identify an existing business problem, study competing solutions and develop a better product.
Make it faster. Make it easier to use. Add functionality. Reduce the price.
For decades, this approach created successful software companies.
But today, AI allows competitors to follow the same strategy much faster and with significantly fewer resources.
If an entrepreneur identifies a problem that everybody already understands, there is a good chance that many others will be able to develop solutions for it.
The result will be stronger competition, lower barriers to entry and increasing pressure on prices.
I think the more interesting question is different.
What problems will exist in five years that companies do not have today?
Because technological revolutions do not only provide new solutions to existing problems.
They also create completely new circumstances, and those circumstances create new problems.
This is the part of the AI transformation that I believe deserves much more attention.
New Technology Creates New Problems
When technology changes how people work, communicate, buy, sell and make decisions, the entire business environment changes.
Existing business processes are redesigned. Responsibilities move between people and systems. New dependencies appear. Expectations change, and rules that worked in the previous environment may no longer be sufficient.
These changes create problems that businesses have never had to solve before.
And very often, the existing software was simply not designed for them.
Consider what happens when AI agents start making business decisions independently.
Software that previously supported a human decision-maker may now execute decisions without direct human involvement.
Who verifies that those decisions are correct? Who determines whether an autonomous action complies with regulations? How does a company demonstrate what happened, why it happened and which rules were applied?
These are not simply old problems requiring faster software.
They are consequences of a new operating environment.
And I believe that the companies capable of identifying such problems early will have some of the most interesting software opportunities ahead of them.
The next generation of valuable software will be built around the problems created by the next generation of technology.
Why I Am Thinking About This in My Own Company
This is not only a theoretical discussion for me.
It is directly connected to how I am thinking about the future strategy of Fiscal Solutions.
For more than two decades, we have worked on fiscalization and regulatory compliance for retailers and POS software vendors across many countries.
Our technology helps companies meet fiscal requirements, process transactions correctly and connect their systems with the necessary compliance infrastructure.
These problems are real, and they will continue to exist.
But AI is changing the technology used to solve them. Integrations will become easier to develop. Software development will become faster. Some functionalities that previously required specialized development may become increasingly standardized.
If we concentrate only on making our existing solutions faster and more efficient, we will certainly improve our products.
But will that be enough to create the next generation of value?
I do not think so.
That is why I am increasingly interested in an idea we call Compliance Intelligence.
The concept is based on a problem that does not fully exist yet.
Today, companies still manage most compliance processes through systems, predefined rules and human responsibilities. But imagine a retail environment in which autonomous AI agents make purchasing decisions, execute transactions, interact with suppliers and operate across different countries.
The complexity of compliance could change fundamentally.
Companies may need to understand not only whether a transaction was processed according to the rules, but also whether an autonomous decision was permitted, which regulatory requirements applied and how compliance can be demonstrated.
I see Compliance Intelligence as a possible answer to that future challenge.
We are not trying to predict every detail of how this environment will look. Nobody can do that reliably.
But we can recognize the direction of change and start understanding the problems that are likely to emerge.
For me, that is the strategic difference between improving an existing product and preparing a company for a new market.
Future-Proofing a Business Means Predicting Problems
When entrepreneurs discuss future-proofing, they often concentrate on technology.
Should we introduce AI? Should we automate development? Should we replace existing architecture? Which new tools should our developers learn?
All these questions are relevant.
But I believe they belong to the second part of the strategy.
The first part should be understanding how the environment in which our customers operate is going to change.
I would start with five questions.
1. Which circumstances are changing in our customers’ businesses?
Look beyond software. Consider changes in technology, customer behavior, business models, regulation, organizational responsibilities and competitive pressure.
2. Which problems will disappear or become easier to solve?
Be honest about the existing product portfolio. Some problems will remain, but solving them may no longer justify the same price.
An entrepreneur should recognize this before the market forces the conclusion.
3. Which new problems will these changes create?
This is where I would invest the most attention. New business processes bring new dependencies, risks and requirements.
Some of these problems will be difficult to recognize because customers have not experienced them yet.
4. Which of these problems will become important enough that customers are willing to pay for a solution?
Not every new challenge represents a business opportunity.
A valuable opportunity exists when the problem becomes sufficiently important, costly or risky that customers need a reliable solution.
5. What knowledge do we already have that gives us an advantage in solving these future problems?
This is particularly important for established software companies.
Years of industry experience, customer relationships and knowledge of business processes may become even more valuable when used to understand new challenges.
The opportunity is not necessarily to abandon everything a company has built.
It may be to use that experience to identify problems that new competitors do not yet understand.
The New Competitive Advantage of an Entrepreneur
I believe AI will create an interesting change in what makes software entrepreneurs successful.
For a long time, being able to develop software was itself an important advantage.
Then the advantage shifted increasingly toward developing better software, faster and at a lower cost.
Now, as AI makes software development more accessible, I believe another capability will become increasingly important.
Recognizing valuable problems before they become obvious to everybody else.
This requires something different from technical development skills.
It requires understanding an industry, recognizing how circumstances are changing, questioning existing business models and imagining the consequences of developments that have not yet reached the market.
It also requires accepting uncertainty.
Developing software for an existing problem is relatively comfortable. The customer understands the problem, competitors demonstrate that a market exists, and the requirements can usually be described.
Developing a solution for an emerging problem is much less comfortable.
Sometimes the customer cannot describe the problem because the circumstances creating it have not fully developed.
There may be no established market, no clear product category and no reliable forecast.
But I believe that is precisely where entrepreneurs can create significant new value.
The challenge is to distinguish a future problem worth preparing for from a prediction that will never become commercially relevant.
That requires continuous contact with customers, industry knowledge and a willingness to test assumptions before committing large resources.
The Biggest Risk Is Developing Yesterday’s Solutions More Efficiently
There is one consequence of AI that I find particularly interesting.
A company can become significantly more productive while simultaneously becoming less competitive.
It can introduce AI into software development, reduce development costs, deliver functionality faster and improve existing products.
All of that can be successful from an operational perspective.
But if the company is improving solutions whose commercial value is declining, greater efficiency will not necessarily secure its future.
It may simply become very good at developing something the market is increasingly unwilling to pay for.
For entrepreneurs, this is an uncomfortable possibility.
We have spent years building products, teams and organizations around problems that we understand very well. Naturally, we want to continue improving those solutions.
But technological revolutions do not protect established business models.
They change the environment in which those models operate.
And a strategy based exclusively on improving the past may become a serious limitation when the future requires something fundamentally different.
The Value Is in the Problem
I do not believe that the future of software is disappearing.
I believe the location of its value is changing.
Software development will become increasingly accessible. More people will be able to create applications, automate processes and build solutions that previously required significant technical resources.
That will put pressure on the value of many existing software products.
At the same time, the changes created by AI will introduce new business processes, new responsibilities, new risks and new challenges.
Those problems will need solutions.
And software will remain one of the most important ways to provide them.
For me, the strategic task of an entrepreneur is therefore not simply to understand how AI can help develop better software.
It is to understand how AI will change the world in which that software will operate.
That is also how I am thinking about the future of my own company.
I want us to continue improving the solutions our customers need today, while investing more attention in understanding the problems they will face tomorrow.
Because I believe the most successful software companies of the next decade will not necessarily be those that write the best code or develop the most features.
They will be the companies that recognize important new problems early enough and develop reliable solutions when the market starts to need them.
The value of software was never in the code itself. It was always in the problem the code solved.
And the next great software opportunity may be a problem that does not exist yet.
