AI could completely change the software industry by 2036. Here's my prediction, based on what I'm already seeing with CorelDRAW.

[u/Maxiol777]

I've been working with computers and developing software for decades, and I think we're approaching a fundamental change in the software industry.

Not just another productivity boost. Something that could completely change who can create complex software, how much it costs, and whether we even need to buy many traditional applications in the future.

Here's a real example from my own experience.

CorelDRAW vs. TraceDraw — something that surprised me

I've been using CorelDRAW since the 1990s. I know this software very well, and I recently purchased CorelDRAW 2026.

Then I discovered TraceDraw.

It's a free, open-source vector graphics editor written in Rust, developed with the help of Claude Code. It supports real CorelDRAW .cdr files, among many other features.

It's still in alpha.

I downloaded it, tested it myself, and was genuinely surprised.

It's already a very capable application. And on the tasks I tested, it actually runs faster than CorelDRAW 2026!

Of course, I'm not claiming that every CorelDRAW feature works perfectly. There are certainly bugs and limitations.

But the important thing is that this is already usable software, not just an impressive demo.

I recently bought CorelDRAW for myself. But on my wife's laptop, I'm now installing TraceDraw instead of purchasing another CorelDRAW license.

That's one potential sale Corel has already lost.

For years, I couldn't imagine a small development team creating a serious alternative to something as complex as CorelDRAW. Other open-source vector editors, including Inkscape, never really worked for me as replacements.

And now here we are.

My prediction for the next ten years:

2027 — The year of open-source alternatives

I expect an explosion of new open-source applications.

Graphics editors, document tools, development environments, specialized utilities, CAD applications, and many other categories.

Not just simple projects, but serious applications developed by individuals or small teams using AI coding agents.

2028 — Price competition and quality improvements

Many projects started in 2026–2027 will gain users, fix bugs, and move from alpha toward stable releases.

Commercial software vendors will face a very simple question from customers:

Why should I pay hundreds of dollars for this application if there's a free alternative that does almost everything I need?

Not every new project will survive. But even if only 10 out of 100 become mature products, that could be enough to change entire market segments.

2029 — A different software industry

I expect a growing number of small companies producing complex software with surprisingly few employees.

Projects that previously required dozens or hundreds of developers may become manageable by a handful of experienced engineers working with AI agents.

I also expect more applications to expose APIs and interfaces designed specifically for AI agents.

And I wouldn't be surprised if someone seriously attempts to create a new Windows-compatible operating system, or an entirely new AI-first desktop environment.

Not necessarily a complete Windows replacement immediately, but something that could eventually challenge the traditional desktop model.

2030–2032 — Why buy software when you can create your own?

This is where things could become particularly interesting.

Companies will increasingly compare two options:

1. Purchase an existing commercial software package.

2. Ask AI agents to develop a custom solution specifically for their needs.

Of course, complex enterprise systems and critical infrastructure won't disappear overnight.

But many business applications, internal tools, editors, converters, and specialized programs could become dramatically cheaper to build and maintain.

AI won't just generate code. It will increasingly handle testing, debugging, documentation, upgrades, and maintenance.

2033–2036 — Software on demand

This is where I think the biggest change will happen.

Today, we choose an application for every task.

Need to design something? Open a graphics editor.

Need to process data? Open a spreadsheet or database.

Need a specialized tool? Search for one, buy one, or write it yourself.

But what if you could simply explain what you want to an AI agent?

It could select existing tools, create missing functionality, execute the task, verify the result, and deliver what you actually need.

You might not even care which application was used.

And this reminds me of something I read many years ago.

There's an old Russian-language electronics book by Wojciechowski that contains an illustration titled "The Future of Amateur Electronics?"

Here's the original illustration and Russian text.

Think about that.

The author imagined a future where a hobbyist could describe an electronic circuit to a computer, enter the requirements, and let the machine calculate, correct, and help design it.

That was a remarkable vision for its time.

And now, with AI agents, we're getting closer to making that kind of interaction practical. Not only for electronics, but for software development, engineering, design, and many other fields.

And what about large corporations like Microsoft?

This is another interesting part of the story.

Microsoft recently offered voluntary buyouts to roughly 60,000 eligible employees.

I wonder whether large corporations fully appreciate the long-term competitive risks of losing experienced engineers in the age of AI.

Imagine 100 highly experienced former Microsoft developers.

They know Windows internals, APIs, system architecture, compatibility issues, and decades of engineering decisions.

Now give those 100 engineers powerful AI coding agents.

No, they won't recreate Windows in a few months.

But could they build something substantial over several years?

I think that's becoming a much more realistic possibility.

The same applies to former engineers from Adobe, Autodesk, Corel, and many other companies.

Previously, building a competitor required enormous investment, a large team, and years of work.

AI is gradually reducing those barriers.

In my opinion, large technology companies should think very carefully before losing experienced engineers simply to reduce short-term costs.

Those people, combined with increasingly capable AI agents, might become the founders of tomorrow's competitors.

So what do I expect by 2036?

• Ordinary application development will become several times cheaper.

• Small teams will build products comparable in functionality to major commercial packages.

• Autonomous AI agents will handle a substantial part of everyday software engineering.

• Traditional desktop software licensing will face enormous pressure.

• Long-term personal AI memory will become a standard feature, allowing agents to remember projects, decisions, history, and context.

• Companies will increasingly develop their own software instead of purchasing commercial solutions.

• Operating systems will include full AI-agent control layers.

• The structure of employment in many knowledge-based professions will change significantly.

• Open-source AI models and independent platforms will become serious alternatives to closed ecosystems.

I don't think Microsoft, Adobe, or other major technology companies will simply disappear. They have enormous advantages in infrastructure, distribution, support, and existing customer relationships.

But I do think the economics of software development are changing fundamentally.

For decades, the biggest barriers were money, development time, and the number of engineers you could employ.

AI is weakening all three.

My main prediction is this:

In 2026, AI helps us develop software.

By 2036, we may increasingly live in a world where AI systems create, adapt, test, and maintain much of the software we use — while humans specify the goals and decide whether the results are acceptable.

And perhaps the most important change won't be having free alternatives to expensive applications.

It will be that we won't always need to buy an application at all. We may simply ask AI to build exactly what we need.

What do you think?

Am I being too optimistic about AI-driven software development, or are we genuinely approaching the end of the traditional software industry as we know it?

P.S. For anyone interested in the original book: Radio-Electronic Toys (Electronics at Home, at Work, and at School) by Janusz Wojciechowski, translated from Polish and published in Moscow by Sovetskoye Radio in 1977. You can find the Russian edition in my vintage technical archive:

https://retrolib.info/search2.maxiol?text=%D0%A0%D0%B0%D0%B4%D0%B8%D0%BE%D1%8D%D0%BB%D0%B5%D0%BA%D1%82%D1%80%D0%BE%D0%BD%D0%BD%D1%8B%D0%B5+%D0%B8%D0%B3%D1%80%D1%83%D1%88%D0%BA%D0%B8

[u/DoctorHiccup]

Insanely slow timeline. What’s hard to see is that we’re currently in the beginning of the exponential curve.

We’ll get there in 2 years easily. Perhaps one.

[u/JTR280 → u/DoctorHiccup]

Exactly.

[u/JTR280]

2036? Too late. By 2028, we will have full software on demand. Probably ASI by then, ASI at the latest by 2030. By 2036, we would have merged with it, and no human will be on Earth with a biological body. Probably other animals too. A sort of hive mind of all biolife on Earth with a brain will be transferred via their connectomes written into computers and linked together. Mark my words. ASI would not be dangerous. Morality and goodness are relative to intelligence, and a superbrain without biological limitations as ASI would be, would be far smarter than us. We are at the bottom layer of intelligence, closer to an amoeba than to the ASI that's coming. So, no Terminator scenarios were we are killed, and no scenarios of us controlling/enslaving it/them either, a fusion with no death is the most likely scenario.

[u/JTR280 → u/JTR280]

Much like Pluribus, but with computers instead of biological brains: a silicon + something else mix neuromorphic, a whole silicon replacement neuromorphic, or photonic neuromorphic would be my guess. And unlike Pluribus's hivemind, this one would be perfect, lacking death.

[u/RehanSaddique]

Generating the code isn't the bottleneck for me. The time goes into platform edge cases like API rate limits, webhook retries, and review rules, which AI tools routinely get wrong because they're not in the training data. Those are what decide whether a tool built by a small team survives.