AI, the Future of IT, and Why I Believe the Industry Is Heading Toward Another Cycle - DEV Community

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Boris

Posted on Jul 13

AI, the Future of IT, and Why I Believe the Industry Is Heading Toward Another Cycle

#ai #development #programming #agents

Over the past two years, artificial intelligence has arguably become the most talked-about topic in the IT industry. It's now almost impossible to open a tech news feed without seeing another headline claiming that AI will soon replace software engineers, that yet another startup has raised millions of dollars thanks to AI, or that a major technology company has switched its employees to AI coding agents while reducing the size of its engineering teams.

At the same time, the software engineering job market has been going through one of its most challenging periods in recent years. The number of open positions has declined, entry-level developers are finding it increasingly difficult to land their first job, and even experienced engineers are discovering that recruiters are no longer reaching out as aggressively as they did just a few years ago. Finding a new position can now take months.

This raised an obvious question for me: is artificial intelligence really the primary reason behind these changes, or are we witnessing several independent trends that simply happened to converge at the same time?

Over the past few months, I've read dozens of industry reports, engineering blogs, interviews with technology executives, and research papers on the adoption of generative AI in software development. The more I learned, the clearer it became that reality is far more nuanced than the headlines suggesting that AI is about to replace software engineers overnight.

In this article, I'd like to bring those pieces together. We'll look at how some of the world's largest technology companies are integrating AI into their engineering workflows, what measurable benefits they've reported, what unexpected challenges their development teams have already encountered, and why more and more people are beginning to ask whether we're witnessing an AI bubble. Finally, I'll share my own perspective on how these trends could shape the software engineering industry over the next several years.

Before we begin, I'd like to make one thing clear: this article is not presented as absolute truth. Some of the material is based on publicly available data published by large technology companies, some comes from industry research, and the conclusions are ultimately my own interpretation of what's happening today. It's entirely possible that a few years from now some of my predictions will prove too optimistic—or too conservative.

But at a time when our industry is evolving almost monthly, I believe it's worth stepping back, connecting the dots, and trying to see the bigger picture.

How Artificial Intelligence Has Changed Software Development

Just a few years ago, most software engineers viewed AI as an interesting technological breakthrough rather than a practical tool for everyday work. Early versions of GitHub Copilot could autocomplete individual lines of code, while ChatGPT was useful for explaining error messages or generating small scripts. At the time, however, serious discussions about AI replacing software engineers still felt premature.

Everything changed rapidly in 2023 with the release of GPT-4, followed by Claude 3, Gemini, Cursor, Windsurf, and a new generation of AI-powered development tools. For the first time, developers could do far more than generate isolated code snippets. They could have an ongoing conversation with a language model, iteratively refining solutions, fixing bugs, discussing architectural decisions, and even planning the structure of an entire application.

The shift became even more significant with the emergence of AI coding agents. Unlike traditional chatbots, which simply answered questions, these agents could actively interact with a codebase. They were able to analyze existing projects, create new files, execute terminal commands, run test suites, fix compilation errors, and even open pull requests automatically. In many ways, large language models evolved from search assistants into active participants in the software development process.

For many engineers, this fundamentally changed the way they work. Instead of writing every function from scratch, developers increasingly describe the problem in natural language and receive a working implementation that can then be reviewed, refined, and integrated into an existing codebase. As a result, the role of the software engineer is gradually shifting away from writing code toward defining requirements, validating quality, making architectural decisions, and ensuring that generated solutions fit the broader system.

This transformation explains why virtually every major technology company is investing enormous resources into AI. Microsoft continues to expand GitHub Copilot, Google is integrating Gemini across its ecosystem, Amazon is developing its own AI-powered engineering assistants, Meta is embedding generative AI into internal workflows, while OpenAI and Anthropic are effectively competing to become the primary platform powering the next generation of AI-assisted software development.

The business perspective has evolved just as dramatically. In 2023, many organizations were still running pilot projects and cautiously evaluating the potential of generative AI. By 2025 and 2026, however, the conversation had shifted. Executives were no longer asking whether AI should be adopted—they were debating how deeply it should be integrated into engineering workflows and how much additional productivity it could unlock. For many organizations, AI has moved beyond experimentation and become a core part of their long-term engineering strategy.

Perhaps the most interesting development is that major technology companies have begun publishing concrete results from their AI adoption efforts. Instead of making vague claims about an "AI revolution," they now share measurable engineering metrics: increases in pull request volume, faster product delivery, higher developer productivity, and significant reductions in time spent on repetitive coding tasks.

These real-world examples explain why AI has become one of the defining technologies of the past few years. Before discussing the challenges, the AI bubble, or the future of software engineering, it's worth examining how the industry's largest companies are already using AI today. That's exactly what we'll explore in the next section.

How the World's Largest Tech Companies Are Adopting AI

When it comes to integrating AI into software development, every company is taking a slightly different approach. Some focus primarily on improving developer productivity, others are redesigning their internal engineering processes, while a growing number now consider AI proficiency to be a fundamental professional skill.

One of the most striking examples is Shopify. In the spring of 2025, the company's CEO published an internal memo that quickly became public. It stated that using AI should become a core competency for every employee. More importantly, before requesting additional headcount or opening a new position, managers were expected to demonstrate that the work could not be completed effectively with AI assistance. In other words, Shopify fundamentally changed the way it approaches hiring and resource planning: AI first, hiring second.

Duolingo adopted a similar strategy by announcing its transition toward an AI-first organization. The company explained that future business processes would be designed with AI capabilities in mind, and that effectively using AI would become one of the key skills expected from employees. Although Duolingo later softened some of its initial messaging after receiving public criticism, its overall direction remained unchanged: AI is now considered an integral part of everyday work.

At Microsoft, AI is no longer viewed as a standalone product. GitHub Copilot has become a core component of the company's engineering ecosystem and is used by millions of developers worldwide. Microsoft executives have repeatedly stated that AI should become as essential to software engineers as modern IDEs or version control systems. Rather than replacing developers, Copilot is positioned as a tool that enables them to work more efficiently.

Amazon has expressed a similar vision. According to the company's leadership, generative AI is being introduced across nearly every business unit. The expectation is that relatively small engineering teams will eventually be able to deliver work that previously required significantly larger organizations. From Amazon's perspective, AI is primarily a tool for improving productivity and optimizing operational costs.

Meta has taken a somewhat different approach. Instead of focusing exclusively on code generation, the company emphasizes automating internal engineering workflows. AI is used to review documentation, process repetitive internal requests, assist with parts of the code review process, and automate numerous routine tasks, allowing engineers to concentrate on more complex technical problems.

Perhaps the most interesting example comes from Spotify. Rather than relying solely on third-party AI tools, the company developed its own internal AI engineering platform called Honk. According to publicly available information, the vast majority of Spotify engineers regularly use AI models and coding agents as part of their daily workflow. Since introducing these tools, the number of pull requests created by developers has increased by approximately 76%, while internal AI agents automatically generate and merge thousands of code changes across the company's repositories. Spotify's engineering leadership has openly stated that the primary bottleneck is no longer writing code—it's reviewing, validating, and maintaining the ever-growing volume of generated changes.

The same trend can be observed across the broader industry. Google has reported that a significant portion of newly written code is now generated with AI assistance. Anthropic has stated that Claude is responsible for producing a large share of the company's internal code, contributing to substantial productivity gains. Meanwhile, monday.com has publicly reported developer productivity improvements of more than 30% following the large-scale adoption of generative AI.

Looking across all these examples, one common pattern becomes clear. At this stage, very few companies openly discuss replacing software engineers with AI. Instead, their primary objective is to increase the productivity of existing teams, automate repetitive work, shorten development cycles, and reduce operational costs.

At first glance, it might seem that AI is solving many of software engineering's long-standing challenges. However, as development velocity continues to increase, a new set of problems has begun to emerge. The faster organizations adopt AI, the more they encounter unexpected side effects: growing technical debt, increasingly complex codebases, heavier code review workloads, and new maintenance challenges that barely existed just a few years ago.

Ironically, these engineering challenges have become some of the most interesting conversations taking place across the industry today. Before discussing the future of software engineering as a profession, it's worth understanding the practical problems AI has already introduced into modern software development.

The AI Bubble: An Overhyped Trend or the Next Industrial Revolution?

As AI becomes increasingly embedded in software development, another question has started gaining traction—one that seemed premature just a few years ago:

Are we witnessing the formation of an AI bubble?

Opinions are deeply divided.

Some believe AI represents the most significant technological breakthrough since the rise of the internet. Others argue that the market has begun assigning hundreds of billions of dollars in value to AI companies long before many of them have established sustainable business models.

These concerns are not unfounded.

Over the past two years, the world's largest technology companies have announced record-breaking investments in AI infrastructure. Microsoft, Google, Amazon, and Meta are collectively spending tens of billions of dollars every year building new data centers, purchasing NVIDIA GPUs, and developing increasingly powerful foundation models. Combined, these investments already amount to hundreds of billions of dollars.

At the same time, valuations across the AI industry have continued to climb at an extraordinary pace. OpenAI has evolved from a research lab into one of the world's most valuable private companies in just a few years, while Anthropic has raised billions in funding and achieved one of the fastest growth trajectories the software industry has ever seen.

At first glance, these valuations may appear justified. Millions of people use ChatGPT, Claude, Gemini, and other AI assistants every day, while enterprises continue integrating generative AI into software development, customer support, search engines, healthcare, finance, manufacturing, and countless other industries.

However, there's an important nuance.

Despite generating billions in revenue, many companies building large AI models remain unprofitable. A significant portion of their income is immediately reinvested into training newer models, expanding computing infrastructure, purchasing GPUs, and covering the enormous operational costs required to serve increasingly capable AI systems. Every new generation of frontier models becomes more expensive to build—and often even more expensive to operate.

Another reason the "AI bubble" discussion has become so widespread is that today's market is largely driven by expectations rather than current financial performance. Investors are valuing AI companies based on the size of the market they could dominate five or ten years from now, rather than on today's earnings. The technology industry has experienced similar periods before, most notably during the dot-com boom of the late 1990s, when market expectations dramatically outpaced business fundamentals.

That said, comparing today's AI industry directly with the dot-com bubble would be an oversimplification.

Unlike many internet startups of that era, today's leading AI companies already have real products, millions of active users, and substantial recurring revenue. AI is no longer limited to chatbots — it is actively transforming software engineering, search, healthcare, scientific research, finance, manufacturing, cybersecurity, and many other industries.

It's also worth remembering that much of today's investment is not limited to language models themselves. A considerable share is going into long-term infrastructure—data centers, cloud platforms, networking, and large-scale computing clusters. Even if the current wave of AI enthusiasm eventually fades, this infrastructure is likely to remain valuable for many years.

Personally, I don't believe the biggest risk is whether the AI bubble eventually bursts. The more important question is which companies will use today's wave of investment to build sustainable businesses — and which ones will become overly dependent on the hype surrounding AI.

History shows that nearly every major technological revolution goes through a period of excessive optimism. We've seen it with the internet, cloud computing, mobile applications, cryptocurrencies, and many other innovations. When the excitement fades, the technology itself rarely disappears. Instead, weaker companies and unrealistic business models vanish, while the underlying technology quietly becomes another essential part of everyday life.

While investors are still debating whether today's AI valuations are justified, the software engineering job market is already experiencing the effects of this transformation. In many ways, the labor market is responding much faster than the financial markets — and that's where the real impact of AI is becoming impossible to ignore.

How AI Is Reshaping the Software Engineering Job Market

If you follow discussions within the software engineering community, it's easy to conclude that every challenge facing today's job market is the direct result of AI. In reality, the picture is far more complex.

The slowdown began well before the widespread adoption of generative AI. Following the hiring boom during the COVID-19 pandemic, many companies found themselves under pressure to reduce costs. Rising interest rates, declining venture capital activity, reduced startup funding, and a more cautious business environment all contributed to a cooling job market. The severe talent shortage that had defined the previous decade gradually began to disappear.

It was at precisely this moment that modern AI tools entered the industry.

For businesses, the timing couldn't have been better. Instead of continuously expanding engineering teams, companies suddenly had an opportunity to increase the productivity of the developers they already employed. Tasks that previously required hiring additional engineers could now be completed—at least partially—with the help of AI coding assistants and autonomous agents.

The impact has been most visible at the entry level.

Traditionally, junior software engineers built their careers by working on relatively straightforward tasks that delivered value to the business while helping them gain practical experience. Bug fixes, CRUD services, automated tests, documentation, simple APIs, and small legacy code refactoring tasks served as the industry's training ground. This is where most engineers learned how real software projects work.

Ironically, these are exactly the kinds of tasks that modern language models perform remarkably well.

Today's AI systems can generate boilerplate code, write unit tests, produce documentation, and fix relatively simple bugs in minutes. As a result, many companies are choosing to augment small teams of experienced engineers with AI rather than opening additional junior positions.

This helps explain why breaking into the industry has become significantly more difficult than it was only a few years ago. Competition for internships and entry-level roles continues to increase, while employers expect candidates to demonstrate a much broader skill set—even before gaining their first professional experience.

However, this trend has another, far less obvious consequence.

Every senior software engineer was once a junior developer. Experienced engineers do not appear overnight after graduating from university or completing an online course. Developing strong engineering judgment requires years of working on production systems, participating in code reviews, fixing real-world bugs, understanding complex architectures, and learning alongside more experienced teammates.

If the industry gradually removes many of these entry-level opportunities, fewer developers will have the chance to follow that same path.

This creates a paradox.

On one hand, companies increasingly need experienced engineers capable of designing scalable architectures, making technical decisions, and effectively leveraging AI-powered development tools.

On the other hand, many of the tasks that historically helped junior developers become senior engineers are now being automated by those very same AI systems.

The result is a long-term talent pipeline problem. Businesses may reduce demand for junior developers today while simultaneously shrinking the pool of engineers who would have become senior contributors five or ten years from now.

In my opinion, this could become one of the most significant long-term consequences of widespread AI adoption. If the current trend continues, today's oversupply of developers could eventually turn into a shortage of highly experienced engineers. Building engineering expertise takes years of hands-on experience—a process that cannot be accelerated as easily as generating code.

Paradoxically, this may also become one of the factors that drives the industry's next hiring cycle.

The history of software engineering has always been cyclical. Periods of aggressive cost optimization are eventually followed by renewed growth. If companies significantly reduce investment in junior talent today, they may face a shortage of experienced engineers several years from now. That shortage could once again increase demand for software engineers and trigger a new wave of hiring across the industry.

The job market, however, is only one area where the effects of AI are becoming visible. As engineering teams continue producing dramatically more code than ever before, many organizations are discovering that the real challenge is no longer writing software—it's maintaining it.

And that's exactly where the next chapter of this story begins.

The Hidden Cost of AI-Assisted Development: When Writing Code Becomes Easier Than Maintaining It

If you listen to engineering leaders at major technology companies, one trend becomes immediately apparent. Almost no one disputes that AI has dramatically accelerated software development. Instead, a different question is starting to dominate engineering discussions:

What do we do with the enormous amount of code we're now producing every day?

The core issue is surprisingly simple. While the speed of writing code has increased dramatically, the speed of understanding, reviewing, testing, and maintaining that code has barely changed.

Not long ago, implementing a new feature could take a developer hours—or even days. Today, an AI coding agent can generate an equivalent implementation in just a few minutes. But generating code is only the beginning of the engineering process.

Every line of code still needs to go through code review, automated testing, architectural validation, and verification against business requirements. As a result, the primary bottleneck in modern software development is gradually shifting away from code generation toward the team's ability to validate code quality.

One of the first consequences has been a dramatic increase in the workload associated with code reviews.

After adopting AI-powered development tools, many companies reported a substantial increase in the number of pull requests submitted by developers. Spotify, for example, publicly shared that AI adoption significantly increased pull request volume across its engineering organization. AI coding agents also tend to generate many small incremental changes instead of a few larger ones, leaving engineers with dozens of review requests rather than a handful of substantial contributions.

At first glance, this may seem like a positive change. Smaller pull requests are generally easier to review. In practice, however, their overall volume grows so quickly that code review itself becomes one of the most expensive stages of the development process.

Not long ago, the industry's biggest challenge was writing software fast enough.

Today, the more pressing question has become:

How do we review all of it?

At the same time, the review process itself has fundamentally changed.

Modern AI agents are remarkably good at producing clean, well-formatted code. They follow naming conventions, maintain consistent formatting, generate comments, and often produce implementations that appear professional at first glance.

Ironically, that's exactly what makes reviewing AI-generated code more difficult.

Reviewers spend less time looking for syntax errors or formatting mistakes and far more time evaluating architectural decisions, verifying business logic, identifying subtle edge cases, and ensuring that new code integrates cleanly with the rest of the system.

In other words, code review is gradually evolving from code verification into engineering validation.

Another growing concern is the rapid accumulation of technical debt.

The cost of writing new code has never been lower. Tasks that once required careful consideration—creating another microservice, introducing an additional abstraction layer, adding a helper library, or building a new internal tool — can now be completed within minutes. As a result, engineers naturally become more willing to create new code than to reuse or simplify existing solutions.

The downside is predictable.

Organizations accumulate more legacy code, more temporary workarounds, more utility services, more abstractions, and more experimental solutions than ever before. Many of these decisions seem perfectly reasonable when they're made. Years later, however, they often become the source of architectural complexity, rising maintenance costs, and increasing developer frustration.

In many ways, AI hasn't eliminated technical debt — it has simply made it much easier to create.

Another challenge is maintaining consistency across a growing codebase. Even when every engineer uses the same AI tool, developers write different prompts, rely on different models, and accept different suggestions. Over time, the same type of functionality may be implemented in several entirely different ways.

As a result, it becomes increasingly difficult to preserve:

A consistent coding style across the organization.
Shared naming conventions.
A coherent project structure.
Documentation and commenting standards.
Consistent error-handling patterns.
Unified architectural principles.

These issues may be almost invisible in small projects. In large products developed by hundreds of engineers, however, they gradually become a significant obstacle. New team members need more time to understand the system, implementing new features becomes increasingly difficult, and experienced engineers spend more of their time cleaning up existing code instead of building new functionality.

Another emerging challenge is the growing dependence on AI tools themselves.

Just a few years ago, software development was primarily constrained by developer capacity, internet connectivity, or the availability of production infrastructure. Today, engineering productivity increasingly depends on the availability of language models and AI coding agents.

Many organizations have integrated AI so deeply into their development workflows that temporary service outages, API failures, or exhausted token quotas can noticeably slow down engineering work. Enterprise teams increasingly operate under fixed token budgets, and during periods of heavy development these limits may be reached well before the end of the billing cycle. When that happens, developers are forced to wait for quotas to reset, switch to less capable models, or purchase additional subscriptions themselves.

This introduces an entirely new category of engineering costs.

Every interaction with an advanced AI model consumes compute resources. An individual developer can generate hundreds of thousands — or even millions — of tokens every day. At the scale of a large engineering organization, that translates into hundreds of millions of tokens daily and significant infrastructure expenses.

As a result, many companies have started measuring not only the productivity gains delivered by AI, but also the monthly cost of operating these systems.

This creates an interesting economic trade-off.

AI undoubtedly enables individual engineers to accomplish far more work than before. At the same time, it substantially increases spending on compute infrastructure and AI services. For certain workloads, the cost of continuously running state-of-the-art models is becoming high enough that the gap between the cost of human engineering and AI-assisted development is no longer as large as many people initially expected.

This is yet another reason why the common narrative that AI will dramatically reduce software development costs deserves a more nuanced discussion.

Increasingly, engineering organizations are arriving at the same conclusion.

AI is exceptionally good at accelerating code generation.

But the more code a team produces, the greater the cost of reviewing it, maintaining it, understanding it, refactoring it, and supporting it over the long term.

In many ways, software engineering has entered a new era — one where the scarcest resource is no longer the ability to write code, but the ability to understand, maintain, and evolve it.

In my opinion, that is the defining challenge of AI-assisted software development — and one that will shape the future of our industry for years to come.

My Perspective: What Comes Next for AI and the Software Industry?

After everything we've covered so far, I'd like to share my personal perspective on where I believe the industry is heading. I don't claim to have all the answers, and these are certainly not predictions set in stone. They're simply my attempt to connect the trends we're already seeing and imagine where they might lead over the next several years.

I don't believe artificial intelligence is just another technology trend that will disappear in a few years. Quite the opposite.

Generative AI is here to stay.

It has already become part of our daily workflow, and I suspect we'll eventually think about it the same way we think about IDEs, Git, CI/CD pipelines, or cloud infrastructure today — not as something revolutionary, but simply as another essential tool that every software engineer uses.

What I do believe, however, is that the current level of excitement surrounding AI is unlikely to last forever.

Today, almost every product is trying to position itself as "AI-powered." Startups attract enormous investments simply by promising to incorporate AI into their products, while the world's largest technology companies continue investing hundreds of billions of dollars into data centers, GPUs, and AI infrastructure.

We've seen this pattern before.

The technology industry has already experienced the dot-com bubble, the blockchain boom, the metaverse hype, and countless smaller technology waves. Each of them went through a period of inflated expectations before eventually settling into a more realistic place within the industry.

For that reason, I wouldn't be surprised if the AI market goes through a significant correction by the end of this decade.

That doesn't mean AI will disappear.

It doesn't mean large language models will suddenly become useless.

What will likely disappear is the hype.

Many companies whose business exists primarily because they can attach the letters "AI" to their products may struggle to survive. Investors will become more selective, funding will increasingly depend on sustainable business models rather than bold promises, and salaries for AI specialists may gradually move closer to the broader software engineering market.

Artificial intelligence will stop being the hottest trend in technology.

Instead, it will simply become another specialization within the software industry.

Ironically, I believe that's when things will become truly interesting.

Over the past few years we've learned how to produce software much faster than ever before.

At the same time, we've also learned how to generate technical debt at an unprecedented pace.

Thousands of new services, internal tools, helper libraries, temporary workarounds, poorly documented modules, and AI-generated implementations aren't going anywhere. They will remain inside banking systems, e-commerce platforms, government services, enterprise applications, and mobile products.

Within just a few years, today's AI-generated code will become tomorrow's legacy code.

Eventually, companies will realize that writing software is only a small part of its lifecycle.

Maintaining that software over the next ten or fifteen years may ultimately cost far more than writing it in the first place.

That's why I expect the industry's focus to gradually shift.

Instead of investing primarily in generating new code, organizations will increasingly invest in maintaining, simplifying, refactoring, and modernizing the enormous codebases created during the first wave of AI-assisted development.

Paradoxically, this may become the catalyst for the software industry's next hiring boom.

If my assumptions are correct, demand for software engineers will eventually grow again — but not necessarily for developers whose primary responsibility is writing new features.

The most valuable engineers will be those who can navigate complex legacy systems, understand large-scale architectures, make sound technical decisions, and gradually transform chaotic codebases into maintainable software.

This is one of the main reasons I don't believe AI will replace software engineers.

I believe it will change what software engineering looks like.

Routine coding will become less valuable. Engineering judgment will become far more valuable.

Architecture.
Systems thinking.
Understanding business requirements.
Making trade-offs.
Communicating with stakeholders.
Taking responsibility for technical decisions.

These are the skills that remain extremely difficult to automate.

So, if you're worried about today's job market, I wouldn't view the current situation as the end of the profession.

More likely, we're simply living through another technology cycle.

Right now, the industry is optimizing.

History suggests that periods like this are almost always followed by new phases of growth.

That future may look very different from the one we knew a decade ago. We'll probably write less boilerplate code, collaborate with AI agents much more frequently, and spend far more time designing systems than typing code.

But I don't believe the need for exceptional software engineers is going away.

And that brings me to what I consider the central message of this article.

Don't think of AI as a magic solution capable of solving every engineering problem.

It doesn't eliminate the need to understand software architecture.
It doesn't replace thoughtful code reviews.
It doesn't prevent technical debt.
And it certainly doesn't take responsibility for the quality of the final product.

Artificial intelligence is just another engineering tool.

A remarkably powerful one.
An incredibly useful one.

But still... Just a tool.

Like every professional tool, it delivers the greatest value only when it's used by someone who understands not only how to use it, but also when to use it — and, just as importantly, when not to.

Conclusion

If you've made it this far, thank you for reading. I hope this article has shown that the story surrounding artificial intelligence is far more nuanced than the headlines often suggest.

A Personal Note

If you enjoyed this article and are interested in software engineering, AI, and the future of the tech industry, I'd be happy to see you on my Telegram channel, ITSisyphus.

There, I regularly write about the latest developments in software engineering, share AI industry news, test new tools for developers, and publish practical content that helps engineers in their day-to-day work — from useful services and libraries to deep dives into modern technologies and engineering practices.

I'm also actively building software with AI coding agents and currently working on halfcoder.ru, a non-commercial project that aims to make developers more productive. The platform brings together a growing collection of engineering tools, including online sandboxes, code generators, DevOps templates, configuration files, practical guides, and developer cheat sheets.

HalfCoder isn't intended as a commercial product — it's a community-driven project that we're continuously improving based on feedback from other developers. If you decide to check it out, we'd genuinely appreciate any suggestions or ideas that could help make it even more useful.

This article exists because I spend a significant amount of time following the evolution of the software industry, reading engineering blogs published by major technology companies, studying research papers, analyzing real-world AI adoption stories, and trying to separate measurable facts from the surrounding hype.

L'impatto dell'AI sullo sviluppo software: oltre l'hype