Software development trends 2025: Are you keeping up?

Software development is in the middle of a step-change. Andrej Karpathy’s framing captures it well: we’ve long passed the point where humans explicitly instructed a compiler in different programming languages (≈ Software 1.0) or even where they shipped software programs by training models instead of hand-coding (≈Software 2.0). Today, we’re in Software 3.0, where AI-assisted development takes the stage and the hottest programming language is… English.

Should you care? After all, companies that are used to clinging to old playbooks don’t vanish overnight. But history is brutal: many stuck-in-their-ways teams eventually dump millions into late pivots while faster players eat their lunch.

That’s why it’s essential to stay on top of what’s happening in the software industry, even if only a tiny part of your business touches it, let alone when you have engineering projects underway. Keep reading to see how you measure up against the latest software development trends in 2025.

1. As AI ‘vibe-ifies’ software development, engineers have to drop the boilerplate and own the vision

Artificial intelligence now sits in every developer’s toolbelt (at least among those who bother to notice what’s happening in the tech industry). Forums and tech blogs have been flooded with posts like ‘My AI fixed the bug before I even saw it.’ Chiseling code with AI copilots has become the new baseline. But if just a year ago all eyes were on AI coding assistants speeding up isolated tasks, today it’s vibe coding that takes center stage.

Coined by (again) Karpathy in early 2025, it expresses the idea of ‘setting the vibe’ and, just like that, getting the app. But jokes aside, what’s really going on is more like agentic-powered coding inside tools like Claude Code or Cursor, where developers prompt the product vision and constraints and agents write code, complete PRs, and push to production while a human oversees the whole process.

Honestly, the split view of the community on this one is understandable. In the hands of newbies who can’t navigate the nuances of coding, agentic tools are basically the devil’s plaything. A junior developer, no matter how eager, just doesn’t have the knowledge and experience to feed LLMs with the “don’t do it this way, it’ll bite you later” kind of vibes. They can write something that technically works, sure. But the solution probably won’t fit certain project goals, won’t scale, and will quickly get overrun with technical debt. The funny part is, they can shove that messy code into production without even realizing it’s messy. 

However, in expert hands these tools are a real force multiplier. Ideas buried in the graveyard of “we need $10-100K just to see if this is even feasible” can now get off the ground in days and at a fraction of the cost. But “the capacity to be a good editor is the reward you get from being a good doer,” after all. So it only works if engineers know their stuff and follow best practices. And yes, even though the field’s still pretty green, some rules of thumb are already clear:

• Breaking tasks down into bite-sized, verifiable chunks and giving crisp, unambiguous instructions
• Doubling down on context: rich prompts, detailed specs, external context sources. Developers should stay in control of what the system sees
• Setting up security guardrails, including sandboxed execution, file system restrictions (allowing read/write only to designated databases and blocking access to sensitive files), running linting and security scanners on generated code, human-in-the-loop approvals, and regular commits
• Acing token math and memory management

In fact, this is arguably what the industry’s future in terms of coding will look like. System architecture design, task planning, context-rich prompt engineering, and orchestration – for the human, mind-numbing drudgery – for AI. It’s a given that most enterprises will run hybrid, pairing AI agents with human engineers in the loop, and such a split in workflow responsibilities and shift in mindset is perhaps one of the standout, can’t-miss trends in software development.

It’s safer to vibe on top of 25+ of old-school coding. Start now

2. Companies are sobering up on artificial intelligence

In 2019-2022, before the major breakthroughs in natural language processing and generation, AI adoption was mostly feature-level. Hyper-personalized recommendations, fraud detection, demand forecasting powered with machine learning, etc. – that was the flavor of most projects. But ever since ChatGPT went public in late 2022, AI has become a boardroom-level obsession for most enterprises.

Leaders greenlit massive investments in AI, whether for building entirely new digital products, restuffing existing ones, or sprinkling AI assistants into daily workflows to speed things up. 

And now? With some of the dust settling, we are not in the place many hoped. Study after study points to sobering results:

• The latest and the most viral one from MIT found that despite roughly $30-40B in enterprise investments into generative AI, about 95% of AI pilot projects haven’t come even close to delivering measurable savings or profit gains.
• Replacing employees with clever bots and agents hasn’t paid off either: 55% of executives regret layoffs made in the name of AI.

So it makes sense that heating talks of an “AI bubble” make investors feel like they’re in troubled waters. Does that signal the end of AI? Hardly. Companies that rushed in headfirst are now moving from hype-driven use to more deliberate, pragmatic AI adoption, and it’s one of 2025’s emerging software development trends.

From our observations and those of industry leaders, two root causes derail AI projects again and again:

1. Too many companies are chasing AI for its own sake. When businesses start with chasing AI instead of defining the problem, it leads to costly projects with no real link to business needs. By contrast, the small share of success stories tells a very different tale. Some pilots went from zero to $20M in revenue within a year. As the above-mentioned MIT report author put it perfectly: “they chose one clear pain point, executed well, and partnered smartly with companies who use their tools”.
2. There’s a severe lack of internal expertise. Many organizations simply don’t know how to use AI tools properly or design workflows that capture value while managing risks. Besides, some large firms, especially in regulated industries, felt compelled to build their own software systems for legal or privacy reasons. Such a “control at all costs” mindset, flavoured by flimsy engineering know-how, led to dead-end initiatives and wasted budgets. 

As of 2025, rather than rushing blindly toward AI, more businesses rely on expert support to identify the most profitable AI use cases, confirm data readiness and context, and only then chart a clear journey from prototype to an MVP to scalable rollout.

3. Software supply chains are being seriously rewired

The whole software development process is moving away from what we’re used to. And AI, being a catalyst of this change, is not running the show alone though.

Broad, AI-enabled overhaul of the software development lifecycle

Given how much AI assistants speed up software development and boost the quality of the final product, it’s no wonder companies are shifting from the traditional development process toward what’s being called an AI-driven SDLC.

Rather than just plugging in smart tools here and there, it takes coordinated action across three levels:

• Strategic: сhoosing the value pools to pursue, defining the outcomes that matter, and deciding where AI will (and won’t) differentiate.
• Operational: building a strong data foundation (prioritizing sources, improving data quality, etc.), investing in AI-powered tools and integrated orchestration platforms, and redesigning processes to embed the tooling end-to-end.  
• Organizational: cultivating AI talent and upskilling existing teams to keep up with shifting labour market demands.

Only such a holistic setup can truly deliver the AI perks everyone’s been buzzing about:

AI lifts the load off the SDLC so software teams can dial into vision and get products out the door quicker, while businesses cut the development costs.

Our experts see, coding is sped up by 60%, drafting docs like user guides or high-level acceptance criteria takes half the time, integration scaffolding slims down by ~80%, and QA reclaims 30-40% to hunt the tricky edge cases.

Enhancing developer experience (DevEx)

Though it definitely plays a role, AI alone can’t dissolve the friction that persists in the SDLC. In 2025, software developers continue to fight against the organizational drag – still getting swallowed by endless email chains, still ping-ponging between pointless meetings, and still digging through scattered documentation to get their work done…. Half of developers lose 10+ hours, while 90% lose 6+ hours, mostly to red tape. For a company with 500 devs, that’s nearly $8M lost annually.

More and more businesses in the software development industry are realizing the need to make life easier for their software developers by cutting through the clutter and putting DevEx front and center. One of the best practices is ramping up platform engineering capabilities via a centralized Internal Developer Platform (IDP), which is basically a hub for APIs, reusable components, infrastructure products, development tooling, documentation, tutorials, demo environments, curated learning paths, and a real-time view of all assets’ statuses, etc.

When a new hire logs in, they click “create project,” pick a ready-made template, and the IDP spins up the environment, hands over API keys, and sets permissions without tickets, meetings, or waiting.

Case in point: After a SaaS provider replaced its patchwork toolchains with a single, secure CI/CD pipeline and baked-in guardrails, 2000 software developers stopped wrestling with infrastructure and started shipping. Code velocity rose 10-20%, the number of critical incidents fell 20%, and security vulnerabilities shrank 15-20 %.

4. Rapid delivery with low-code / no-code tools, now even more rapid with AI capabilities

Gone are the days when a months-long software development cycle was an acceptable price for just an MVP. To be fair, speed has always been table stakes. But what’s different now is that it no longer requires cutting corners on quality.

Against this backdrop, something companies have been desperately waiting for finally hits home: the ability to quickly test whether a software idea actually flies before pouring effort and money into thin air. Prototyping has become lightning-fast.

That’s largely because low-code/no-code platforms (LCNC), once mostly clunky drag-and-drop website builders, have matured and now can assemble complex systems, complete with integrations, APIs, forms, etc., with minimal-to-zero coding required. And they’re quickly embedding AI:

• Back in 2023, the no-code tool Bubble rolled out its Azure OpenAI Service plugin, letting businesses connect their apps to OpenAI’s models. Soon after, support was added for other popular models such as Claude, Grok, and Gemini.
• In October 2024, OutSystems brought out Mentor, an AI-powered digital assistant built to step in with context-aware help across the software development workflow, able to carry out sequential tasks and even take entire processes out of users’ hands.
• And just recently, in July 2025, Microsoft announced a shift in its low-code Power Apps tool toward agent-first app generation, to enable developers to quickly create custom AI agents to manage repetitive tasks. Its integration with Copilot makes the whole process faster by suggesting workflows primed for agentic automation. Yet, this out-of-the-box approach has its limits, and in one of our recent case studies we show how a custom agentic setup helped our client break through those constraints.

Anyway, building a core, heavyweight enterprise system LCNC-only is hardly the wisest strategy. Because the moment a new regulation, merger, or black-swan event forces you to change a core assumption in the solution’s architecture, that change ends up moving at the speed of a full-on manual rewrite anyway. All you can do is export the LCNC-generated code (if the tool even lets you), only to find it’s a spaghetti tangle of platform-specific runtime calls your developers refuse to touch.

But for a lot of non-mission-critical business apps, when there’s no decent off-the-shelf fit, low- and no-code platforms allow getting the job done with a small team of skilled people, who can keep the system evolving, supported, and refined in a low-lift way.

5. Companies are falling out of love with public cloud (hello, giant bills) and moving back to private cloud

A major cloud computing shake-up stands out among the latest software development trends. In the year ahead, we’ll likely see businesses take a hard look at the private cloud again and double down on hybrid stacks (a mix of private – on premises or hosted – infrastructure, edge nodes, and yes, still, some bits of public cloud).

As sweet as cloud providers’  promises of cost efficiency, scalability, and speed sounded made (no wonder forecasts put worldwide public cloud spending near $1.6 trillion by 2028, doubling its 2024 level), the reality of unexpected operational costs hit just as hard.

A recent survey shows 53% of IT decision-makers at companies with 100+ employees overshot their planned cloud storage spend. The main reasons cited are using more storage than planned, migrating more apps and data than expected, and unanticipated egress or API fees.

David Heinemeier Hansson, co-owner and CTO of 37signals, publicly shared his ‘exit the cloud’ story (and reasons behind it). Horrified by seven-figure annual bills, their team abandoned AWS S3 in favor of an on-prem setup. By their calculation, it will cost under $200K per year instead of $3.2 million spent on cloud computing.

But it’s not just the cost that is steering companies off the public cloud. The now chronic geopolitical uncertainty has sparked the sovereign-cloud debate in Europe. Public sector and highly regulated industries, such as finance and healthcare, increasingly require digital autonomy, often meaning private cloud and data residency by country or region. 

On top of that, growing demand for AI inference, machine learning, IoT, and autonomous systems that need ultra-low latency is driving the need for edge computing instead of distant mega-clouds.

6. Digital trust and security are still the backbone of enterprise tech

No matter how many times the “cybersecurity is vital” mantra is drilled in, yet – boom – another CrowdStrike-scale mess hits the fan. The stakes are only getting higher with AI, which is bringing new pressure points for security. 

Companies that once dragged their feet on data management now realize that training large language models requires clean, well-governed, and secure data from the start. Storage, processing, and classification all have to be tightened to make the initiative worth it. As Erin Hughes, Head of Cybersecurity Advisory, North America SAP, notes, CISOs often aren’t the data owners, so security and data teams need shared classification definitions and common rules of engagement, especially for AI.

The challenge doesn’t stop there. As enterprises stitch together sprawling ecosystems of third-party software, many overlook the basics of safe usage and resilience. So, lately,  companies have been getting dead serious about their data security posture, normally through:

• continuous employee education
• robust tech safeguards like identity and access management or multifactor authentication
• continuous threat monitoring
• clear understanding of regulatory obligations and compliance requirements related to the implementation of innovative solutions
• practiced crisis response and recovery plans

As for software development, one of the most defining shifts is integrating security by design. Practically speaking, that means treating DevSecOps less as a buzzword and more as the baseline.

Read also: DevSecOps: How to Integrate Security into DevOps >>

And once the controls are rolled out everywhere, it’s just the beginning. Security mechanisms must evolve alongside advancements in emerging technologies, ideally a step ahead. 

7. Hybrid approach to outsourcing is becoming the default

In 2025, many large corporations still seem convinced they can go back to the “good old days” of office life. They’re tightening their RTO policies, but the toothpaste is already out of the tube. Software engineers who’ve tasted the flexibility of remote work simply aren’t flocking back.

Besides, this whole return-to-office push seems counterproductive for a market where the cohort of senior engineers who can design, debug, and own production-grade AI systems is so tiny that companies struggle to fill those seats.

Regardless of your stance on remote work, one pattern is clear in 2025. When it comes to planning digital transformation initiatives, it is often faster, cheaper, and way more effective to tap into specialized dev shops offering self-managed, distributed development teams with AI-native, best-of-breed technical expertise. 

Most Fortune 500s are realizing just how flexible outsourcing has become. Pragmatic leaders now use hybrid models, keeping some projects in-house while handing off routine work to cost-savvy external distributed teams.

The bottom line

The future of the software development industry is rewritten by AI. It’s agent-powered. It’s security-centered. And… It’s nothing like what we’re used to. Those who grasp it and keep pace with these Formula-1-speed changes are best positioned to ride on competitive advantage.

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