Software companies are dead. Just nobody told them.
Or so everyone keeps saying. LinkedIn is flooded with Claude Code hype. Cursor is “changing everything.” Every founder on your feed is screaming that developers will be redundant by Q2. The revolution is here, they say. Pack your bags.
This is now the third consecutive year of this narrative. Three years of breathless predictions. Three years of “this time it is different.”
And yet, software development companies still exist. Programmers still have jobs. Humans are still an essential part of every serious project. The global software development market hit $824 billion in 2025 and is projected to nearly triple to $2.25 trillion by 2034. The developer population has grown to 47.2 million worldwide, a 50% increase from 2022. Custom software development is expanding at over 20% compound annual growth.
These are not the numbers of a dying industry. These are the numbers of an industry accelerating.
So what exactly is going on with all this hype?
The Pattern We Keep Forgetting
If you have been in this industry long enough, the current AI hysteria feels less like a revolution and more like a rerun. The script is always the same. A new technology emerges. It generates genuine excitement. Then the hype machine takes over, and suddenly the technology is not just useful, it is existential. It will not just improve things, it will replace everything that came before.
We have seen this show before.
In the late 1980s, CASE tools (Computer-Aided Software Engineering) were supposed to automate programming out of existence. Structured methodologies and code generators would make manual coding obsolete. Companies spent billions on these tools. The tools helped with certain tasks. They did not eliminate programmers.
In the 1990s, visual programming and fourth-generation languages promised that business users would write their own software. Drag, drop, done. The idea was compelling. The reality was that anything beyond a simple form required a developer who understood what was happening underneath the visual layer.
In the 2000s, offshore outsourcing was going to kill domestic software companies. Why pay local rates when you could get the same work done for a fraction of the cost? Some work moved. The industry adapted. Software companies in every market continued to grow because the bottleneck was never just the cost of typing code.
In the 2010s, no-code and low-code platforms arrived with the same promise: anyone can build software now. Gartner predicted that 65% of app development would happen on low-code platforms by 2024. These tools found their niche for internal dashboards and simple workflows. They did not replace custom development. The custom software development market is still growing at over 20% annually.
Now in the 2020s, AI coding assistants are the latest candidate for the “thing that finally kills developers.” The pitch is the same. The outcome will be the same. Not because AI is not powerful, but because the pitch fundamentally misunderstands what software development actually is.
The Misconception at the Heart of the Hype
Every wave of “developers are dead” hype shares the same underlying assumption: that software development is primarily about writing code. If you believe that, then any tool that writes code faster, or writes it for you, logically threatens the entire profession.
But software development has never been primarily about writing code.
Writing code is the most visible part of the process. It is also one of the smallest. The actual work of software development is understanding what needs to be built and why. It is translating vague business requirements into precise technical specifications. It is making architectural decisions that will determine whether a system scales or collapses under load. It is debugging not just code but the assumptions behind the code. It is navigating trade-offs between speed, quality, security, and cost. It is communicating with stakeholders who do not know what they want until they see what they do not want.
None of this is about typing syntax faster.
When someone demonstrates an AI tool building a todo app in 30 seconds, the audience sees magic. What they do not see is that the todo app has no authentication, no authorization, no error handling for edge cases, no audit logging, no accessibility compliance, no data migration strategy, no monitoring, no deployment pipeline, and no plan for what happens when the requirements change next week. Which they will.
The demo is impressive. The demo is also the easiest 2% of the job.
What AI Actually Does (and Does Well)
I want to be clear about something: I am not dismissing AI tools. I use them every single day. They are genuinely incredible for certain tasks, and any developer who refuses to use them out of pride or fear is making a mistake.
AI coding assistants are excellent at generating boilerplate code. They handle repetitive patterns faster than any human could. They are useful for exploring unfamiliar APIs, generating test scaffolding, drafting documentation, and suggesting approaches to well-defined problems. They save time. They reduce tedium. They let developers focus on the parts of the work that actually require human judgment.
That is the key distinction. AI tools are force multipliers for skilled developers, not replacements for them.
A skilled developer with AI tools is dramatically more productive than the same developer without them. But an unskilled person with AI tools is not a developer. They are someone who can generate code they cannot evaluate, debug, or maintain. There is a meaningful difference between producing code and building software.
The data supports this. Recent analysis found that 45% of AI-generated code contains vulnerabilities from the OWASP top 10 security risks. In Java specifically, the security failure rate for AI-generated code exceeds 72%. Experienced engineers have reported being slower when using AI tools for complex tasks because the time spent prompting, waiting, and reviewing outweighed the gains. AI-generated pull requests show nearly double the issues compared to human-written code.
These are not reasons to avoid AI tools. They are reasons to understand that AI tools need skilled humans operating them. A chainsaw is more productive than a hand saw, but handing a chainsaw to someone who has never felled a tree does not make them a lumberjack.
The Real Numbers Tell a Different Story
If AI were genuinely replacing software companies and developers, we would see it in the numbers. We would see declining market size, shrinking developer populations, and reduced demand for custom software. We see the opposite across every metric.
The global software development market is valued at approximately $524 billion in 2025, with projections to exceed $1 trillion by 2032 at a compound annual growth rate of over 10%. Worldwide IT spending will reach $5.74 trillion this year, with software spending alone growing 14%. The custom software development market is on track to grow from $43 billion in 2024 to $146 billion by 2030.
The developer population tells the same story. The global count has reached 47.2 million developers, with the professional segment growing 70% since 2022. The US Bureau of Labor Statistics projects 17% compound annual employment growth for software developers, a rate described as “much faster than average.” South Asia has nearly doubled its developer population from 4 million to 7.5 million in three years.
These numbers do not describe an industry being disrupted into extinction. They describe an industry experiencing sustained, accelerating growth, powered in part by the very AI tools that are supposedly killing it.
Why Each Wave Creates More Demand, Not Less
There is a paradox that the “developers are dead” crowd consistently overlooks. Every technology that was supposed to eliminate developers has, in the long run, created more demand for them. This is not a coincidence. It is a structural feature of how technology markets work.
When tools become more powerful, the ambition of what people want to build increases proportionally. CASE tools did not eliminate developers; they enabled more complex enterprise systems that required more developers. Visual programming did not replace coding; it created a new layer of abstraction that still needed programmers underneath. No-code platforms did not end custom development; they showed businesses what was possible, which generated demand for more sophisticated custom solutions that the platforms could not deliver.
AI tools are following the same trajectory. By making it faster to build prototypes and MVPs, they are expanding the market for software. More ideas can be tested. More businesses can explore digital products. More prototypes need to be turned into production systems. And turning a prototype into a production system still requires people who understand architecture, security, scalability, compliance, and the hundred other concerns that separate a demo from a product.
The spreadsheet is perhaps the best historical analogy. When spreadsheets arrived, some predicted they would eliminate the need for accountants and financial analysts. Instead, spreadsheets democratized financial modeling, which dramatically increased the demand for people who could build, validate, and maintain complex models. The tool did not replace the expertise. It amplified it.
AI is doing the same thing to software development right now.
What Is Actually Changing
Saying that software companies are not dead does not mean nothing is changing. Quite a lot is changing, and developers and companies that ignore these shifts will struggle. The changes are just different from what the hype suggests.
First, the value of implementation speed is dropping. When AI can generate functional code in seconds, the ability to write code quickly is worth less than it used to be. The skills that are increasing in value are the ones AI cannot replicate: understanding what to build, defining clear specifications, designing systems that can evolve, and making judgment calls about trade-offs that involve business context, user behavior, and technical constraints simultaneously.
Second, the role of the developer is shifting toward orchestration. The best developers I know today spend less time writing code from scratch and more time directing AI tools, reviewing their output, integrating components, and ensuring that the overall system holds together. This is not a diminishment of the role. It is an elevation. The developer is becoming more of a system architect and less of a typist. That is a good thing.
Third, the bar for entry-level developers is changing. With AI handling much of the boilerplate that juniors used to write, the pathway for new developers looks different. There is a real risk that companies stop hiring juniors because AI handles “junior tasks,” which would be a serious long-term mistake. Today’s juniors are tomorrow’s senior engineers and architects. The pipeline matters, and industry leaders need to think carefully about maintaining it.
Fourth, clarity is becoming the most valuable skill in software development. AI does not struggle with hard problems. AI struggles with vague problems. The developers and companies that can translate ambiguous business needs into precise specifications will extract the most value from AI tools. Those who cannot will produce code that technically runs but does not solve the right problem.
The People Driving the Hype
It is worth asking who benefits from the “software companies are dead” narrative, because the answer explains a lot about why it persists despite the evidence.
AI tool companies benefit from it. If developers are about to be replaced, then every company urgently needs their product. Fear is the most effective sales pitch in technology. “Your competitors are already using this” is the modern equivalent of “nobody ever got fired for buying IBM.”
Founders raising capital benefit from it. Telling investors that AI will reduce your engineering headcount by 70% makes your unit economics look spectacular on a pitch deck. Whether it materializes is a problem for future quarters.
Content creators and influencers benefit from it. “AI will kill developers” generates more engagement than “AI is a useful productivity tool.” Nuance does not go viral. Apocalyptic predictions do.
Management consultants benefit from it. If AI can replace your engineering team, you need a consultant to help you navigate the transition. The transition that never quite arrives, but the consulting engagement continues regardless.
The people who do not benefit from this narrative are the ones building real products for real users. They know the truth, which is that AI makes them faster but does not make them unnecessary. They are too busy shipping to post about it on LinkedIn.
What Smart Software Companies Are Actually Doing
While the commentators predict their demise, the software companies that are thriving have a straightforward approach. They are integrating AI deeply into their workflows. Not as a replacement for their teams, but as infrastructure that makes their teams more capable.
They are using AI to accelerate code reviews, generate test suites, draft initial implementations that experienced developers then refine, and automate deployment pipelines. They are building internal tools that combine AI capabilities with domain expertise. They are training their teams to be effective at working with AI, not to be replaced by it.
The result is not fewer developers. It is the same number of developers (or more) producing significantly more value. Projects that used to take six months are done in three. Features that required a full sprint are delivered in days. Teams that could handle three clients can now handle five.
This is what actually happens when a new tool is powerful: the people who master it become more productive and more valuable. The tool does not eliminate the profession. It raises the ceiling on what the profession can accomplish.
The Uncomfortable Truth for the “Dead” Crowd
Here is what the “software companies are dead” crowd does not want to confront: the 95% failure rate of enterprise generative AI pilots to deliver measurable ROI. The finding that two-thirds of tech leaders who integrated AI have not saved a single human headcount. The analysis showing that AI-generated code has created a wave of technical debt, with one study estimating it would take 61 billion work days to address the world’s current code quality deficit. The reports of a 4x surge in code cloning, producing what engineers call the “slop layer” of code that works but nobody understands.
None of this means AI is failing. It means AI is not doing what the hype promised it would do. It is doing what powerful tools have always done: making skilled people more effective while creating new categories of problems that require skilled people to solve.
The companies that tried to replace their developers with AI are now hiring developers to fix what the AI produced. The companies that used AI to empower their developers are shipping better products faster. The difference between these two outcomes is not the technology. It is the understanding of what software development actually requires.
Software Companies Are Not Dead. They Are Evolving.
The tools have changed. The fundamentals have not.
Software development still requires understanding the problem before writing the solution. It still requires architectural thinking. It still requires someone to make judgment calls about trade-offs that no model can make because the relevant context lives in conversations, relationships, business constraints, and hard-won experience, not in training data.
The developers who are thriving in 2026 are not the ones who write the fastest code. They are the ones who define the clearest specifications, make the best architectural decisions, and know how to leverage AI as a tool rather than surrendering to it as a replacement. They are orchestrators, not typists.
Software companies are not dead. Not even close. They are adapting, as they always have. The ones who understand that AI is an amplifier will grow. The ones who believed the hype that AI is a replacement will learn an expensive lesson. The rest of us will keep building.
That is what we have always done.
Final Words
If this resonated with you, or even if you disagree, I would love to hear your take. The best conversations happen when people with real experience push back, share their own observations, and add nuance to the discussion.
You can follow me on LinkedIn for regular thoughts on software engineering, AI, and the realities of building technology over the long term. I also write on Threads for shorter, sharper takes.
If you want to discuss this topic further, collaborate on something, or just say hello, feel free to reach out. I respond to everything.
What do you think? Are software companies really dying, or are we just watching the same cycle repeat itself with better special effects? Drop me a message. I am genuinely curious what you are seeing from where you sit.