Challenges Facing Software Developers in 2025

Several comprehensive studies including O’Reilly’s Playbook for Large Language Model Security, the 2025 State of Software Delivery report, and GitClear’s 2025 AI Copilot Code Quality report conclude that companies have started using AI for #coding too soon. A general conclusion: “LLMs are not #software engineers; they are like interns with goldfish memory. They’re great for quick tasks but terrible at keeping track of the big picture.” “As reliance on #AI increases, that big picture is being sidelined. Ironically, by certain accounts, the total developer workload is increasing—the majority of developers spend more time debugging AI-generated code and resolving security vulnerabilities.” “AI output is usually pretty good, but it’s still not quite reliable enough,” says another. “It needs to be a lot more accurate and consistent. Developers still always need to review, debug, and adjust it.” One problem: “AI tools tend to duplicate code, missing opportunities for code reuse and increasing the volume of code that must be maintained.” GitClear’s report “analyzed 211 million lines of code changes and found that in 2024, the frequency of duplicated code blocks increased eightfold.” “In addition to piling on unnecessary technical debt, cloned code blocks are linked to more defects—anywhere from 15% to 50% more.” While larger context windows will help, “they’re still insufficient to grasp full software architectures or suggest proper refactoring.” One CEO says: “AI tools often waste more time than they save for areas like generating entire programs or where broader context is required. The quality of the code generated drops significantly when they’re asked to write longer-form routines.” “Hallucinations still remain a concern. AI doesn’t just make mistakes—it makes them confidently. It will invent open-source packages that don’t exist, introduce subtle security vulnerabilities, and do it all with a straight face.” “Security vulnerabilities are another issue. AI-generated code may contain exploitable flaws.” Furthermore, AI agents often “fail to find root cause, resulting in partial or flawed solutions:” “Agents pinpoint the source of an issue remarkably quickly, using keyword searches across the whole repository to quickly locate the relevant file and functions—often far faster than a human would. However, they often exhibit a limited understanding of how the issue spans multiple components or files, and fail to address the root cause, leading to solutions that are incorrect or insufficiently comprehensive.” Solutions include better training data, more testing to validate AI outputs, progressive rollouts, and greater use of finely tuned models. The bottom line for some: “AI-generated code isn’t great—yet. But if you’re ignoring it, you’re already behind. The next 12 months are going to be a wild ride.” #technology #innovation #artificialintelligence #hype

2025's greatest challenge won't be technological—it will be societal. It's not just about having cutting-edge AI anymore. It's about the growing divide between those who harness it effectively and those who don't. This split will define the next wave of innovation and success. We're entering an era where raw technological capability isn't enough. Organizations and individuals who simply acquire AI tools without understanding how to apply them meaningfully will fall behind. The winners will be those who deeply understand their market problems to create massive impact with targeted AI applications. The key differentiator isn't just access to technology—it's the ability to identify and solve genuine problems. This means: ⚡ Understanding your users' pain points deeply ⚡ Applying AI strategically, not blindly ⚡ Building solutions that address specific challenges ⚡ Focusing on outcomes, not just capabilities ⚡ Staying connected to real-world problems As founders become more empowered by AI than ever before, the opportunity landscape is shifting dramatically. But here's the truth: the most successful builders won't be those with the most sophisticated AI models—they'll be the ones who understand their problems so well that they know exactly where and how to deploy AI effectively. Don't get caught up in the AI arms race. Focus instead on becoming the expert in your problem space. Because in 2025, that's what will separate the winners from the rest.

𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗶𝘀 𝗵𝗮𝗿𝗱, and it gets harder at large enterprises where you’re juggling tens (or hundreds) of product teams. What’s changing fast is how software is built: - “Traditional” SDLC security gates are turning into a real velocity bottleneck, especially with broad AI adoption. - Human-in-the-loop review doesn’t scale, but fully autonomous LLM-driven SAST/SCA isn’t deterministic either, and that can mean missed issues or expensive triage cycles full of false positives/non-exploitable findings. So the question I keep coming back to is: how do we adapt product security workflows and processes for this new reality? A lot can be automated with agentic AI, but context gaps are where things get dangerous. Without the right asset, ownership, dependency, and runtime context, automation can create new business risks instead of reducing them. Last week I wrote about this in the context of real-world vulnerability hunting and validation: “Hunting React2Shell (CVE-2025-5518)” (https://lnkd.in/gqrxWFw3). Even today, we’re still doing a ton of manual work for: - variant analysis and discovery of impacted software assets - fix development across diverse codebases - validation (especially when the exploitability boundary is subtle) And it makes me ask something uncomfortable: why are we still seeing the same classes of security problems in 2025 that we dealt with two decades ago? Were all the “next-gen” solutions ever truly next-gen, or are they simply not keeping pace with exponential growth of software complexity? The vulnerability lifecycle hasn’t evolved much, but software development has shifted dramatically: - massive dependency supply chains (OSS + proprietary ecosystems) - distributed ownership and external coordination - and now, AI-assisted development that often turns code into a “black box” behind an AI fog AI boosts development velocity, but it can also widen product security gaps. Vulnerability investigations become more complicated and take longer, because the people responsible for the software may have produced less of the code themselves… which becomes a context bottleneck when you need deep, code-level understanding beyond architecture diagrams. I’m still a big believer in technology and innovation. But the more I see these patterns repeat, the more convinced I'm that we haven’t yet moved product security in the right direction. How are you evolving your vulnerability lifecycle and product security workflows to keep up with AI-driven development velocity without increasing risk?

It's the first Monday of 2026. Here are my 4 predictions for Extend and devtools this year: 1/ The return of specialized models. Latency, cost, and determinism matter again. For a while, everyone was okay with 5-second wait times if the "magic" worked. That's over. We aren't going back to training custom BERT models for everything, but we are seeing a shift away from throwing massive reasoning models at every problem. We won't see companies tuning "legal models" like Harvey for an entire domain, but rather for specific narrow tasks (e.g. handwriting recognition) where running in < 200ms for cheap matters. 2/ Developer products must be "glass box" by default. For technical teams, complete abstraction is a bug. If an API breaks or you reach a performance limit with a vendor, and you can't see why or improve it, you can't deploy it long-term. The best tools will expose the internals: tell me what model you're using, show me the chunking strategy, etc. 3/ AI edge cases will cause companies to move away from pure in-house builds. Tools like Claude Code have driven the cost of writing software to near zero, and getting to ~90% has never been easier. But the last 10% of long-tail edge cases (in doc processing, examples include weird layouts, faint handwriting, 200pg + docs) requires a massive amount of glue code, heuristics, specific pre-processing, and tooling. And as markets get more competitive, that last 10% becomes more important than ever. Teams are realizing that handling that long tail of edge cases is a distraction they don't want to own, and working with the right devtool can accelerate their builds. 4/ Tooling becomes the primary accelerator. The biggest bottleneck to velocity is the fear of regression. In 2025, a lot of teams slowed down because they couldn't confidently touch their prompt chains without breaking 5 other things. Reliable eval sets, guardrails, and LLM-as-a-judge is a must-have and the only way to ship 10x faster. 2026 is going to change faster than 2025 ever did. Let's build.

Python has become the top programming language on GitHub, driven by AI programming, while Sundar Pichai reveals that over 25% of Google’s code is now AI-generated. This isn’t just a productivity boost -- it’s a shift in how the world builds technology. What does this mean for the future of software development? • Faster time to market: AI accelerates development, helping projects launch quicker. But speed must be paired with robust quality control. • Changing developer roles: Developers are evolving into AI collaborators -- crafting prompts, guiding AI models, validating outputs, and integrating machine-generated code into complex systems. This shift requires developers to master new skills like understanding AI model limitations, debugging AI-generated code, and ensuring ethical AI implementation. • New quality standards: AI-assisted coding brings new challenges, requiring updated code review processes, metrics for maintainability, and rigorous validation of AI-generated snippets. This includes developing new testing methodologies specifically for AI-generated code and addressing the explainability and interpretability of such code. • Transforming education: Future engineers will focus on skills like prompt engineering, model evaluation, and system-level thinking, shifting away from traditional coding-only curricula. • Reshaping teams: Smaller, specialized teams may emerge, focusing on orchestrating AI-driven workflows instead of writing every line of code manually. • The rise of natural language programming: As AI tools rely heavily on natural language prompts, programming itself may shift from traditional syntax to conversational interaction. This raises a critical question: will English's dominance in these interactions widen the accessibility gap or democratize coding for a global audience? • Ethical challenges: AI-generated code raises concerns about intellectual property, accountability, biases, safety, and security. Ensuring licensing compliance, mitigating inequities, addressing vulnerabilities, and building transparent frameworks will be critical to balancing innovation with responsibility. With AI fundamentally transforming software development, are we ready to navigate this new era of opportunity, challenges, and responsibility? #CodingWithAI #FutureOfCoding #ReponsibleAI

Having led VTEST through numerous tech transformations, I've noticed critical shifts in software quality that aren't making headlines. While everyone's focused on AI testing tools, we're facing unprecedented challenges in microservices testing, security integration, and even environmental impact assessment. In my latest article, I break down the less discussed but crucial trends that will define software quality in 2025. From the complexities of distributed system testing to the emergence of environmental impact metrics, these changes will fundamentally reshape how we approach quality assurance. Curious to hear your thoughts: Which of these trends is already impacting your testing strategy? #futuretesting #ai #techtrends # #softwaretesting #qualityassurance #digitaltransformation #performanceresting #aiintesting #agiletesting #devops #continuoustesting #testing #testautomation #testers #testingjobs #techleadership #digitaltransformation #softwaretestingcompany #softwaretestingservices #awesometesting #vtest VTEST

𝐒𝐭𝐚𝐜𝐤 𝐎𝐯𝐞𝐫𝐟𝐥𝐨𝐰 𝐒𝐮𝐫𝐯𝐞𝐲 𝟐𝟎𝟐𝟓: 𝐀𝐈 𝐢𝐬 𝐇𝐞𝐫𝐞, 𝐁𝐮𝐭 𝐃𝐨 𝐖𝐞 𝐓𝐫𝐮𝐬𝐭 𝐈𝐭? Is "𝐕𝐢𝐛𝐞 𝐂𝐨𝐝𝐢𝐧𝐠" the new normal? The data says absolutely not. I’ve just gone deep into the Stack Overflow Developer Survey 2025 results (49,000+ respondents), and the biggest takeaway is a massive reality check for the industry. In 2024, the hype was that AI would write everything for us. In 2025, the data shows we are using AI more, but 𝐭𝐫𝐮𝐬𝐭𝐢𝐧𝐠 it less. Here are my top 5 takeaways from the survey that every student and professional developer needs to know: 1. The AI Trust Paradox 84% of developers now use AI tools (up from 76%). Yet, only 33% trust the accuracy of the output. We are treating AI like an overconfident junior developer—it’s helpful, but you must double-check every line. 2. The 𝐏𝐲𝐭𝐡𝐨𝐧 Explosion Continues If you were doubting Python, stop. It saw a massive 7% jump in usage this year, closing in rapidly on SQL. It is now the undisputed language of the AI era. 3. The Database King: 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 Stop fighting over which database to learn. For the second year in a row, PostgreSQL is the #1 database (56%), beating MySQL (41%). If you are building a modern backend, Postgres is the industry standard. 4. Will AI Take Your Job? The fear index is shifting. 64% of developers say AI is NOT a threat to their job. However, this confidence has dropped slightly from last year (68%). The consensus? AI won't replace developers, but developers who use AI and understand the internals will replace those who don't. 5. The "YouTube to Docs" Pipeline 60% of learners rely on YouTube (which is great!). But 68% of professionals rely on Technical Documentation. To go pro, you must learn to read the docs. The survey proves that fundamentals matter more than ever. AI can generate code, but it cannot architect a system, debug a complex race condition, or choose the right database strategy. Would love to know in the comments: Do you trust Copilot/ChatGPT output 100%, or do you still debug it line-by-line? #StackOverflowSurvey2025 #SoftwareEngineering #Python #PostgreSQL #CareerAdvice #Telusko #AI #ProgrammingTrends

Recent moves in the tech industry have put the spotlight on AI’s growing role in software development. Salesforce has publicly reduced hiring for engineering roles, citing AI-driven productivity. Meanwhile, Mark Zuckerberg has projected that, by 2025, AI systems at Meta could function much like mid-level engineers - writing code and even building other AI tools. Will This Hit Tech Jobs Hard? Mid-Level Roles at Risk? AI is likely to handle standardized coding work - think bug fixes, boilerplate code, and refactoring. This could reduce the need for some mid-level roles. While some positions may be eliminated, new ones may emerge - like AI model trainers, AI code reviewers, or specialists who oversee integrations between AI-generated code and existing systems. Timeline & Reality Check 2025 Feels Ambitious - while technology evolves quickly, complete replacement is rarely immediate. AI might be capable of handling specific tasks, but complex architectural decisions and creative problem-solving still require human oversight. Potential Implications - Skill Realignment: Engineers may need to shift focus, learning how to work alongside AI - reviewing its output, guiding the creative process, and ensuring code quality. - Job Polarization: We might see a growing distinction between high-level “architect” roles requiring strategic thinking and entry-level positions maintaining or customizing AI-generated code. - Ethical & Quality Concerns: AI-generated code can inadvertently introduce errors or biases. Ensuring accountability, robustness, and alignment with organizational goals remains a uniquely human responsibility. Yes, there’s a strong push toward using AI for mid-level development tasks. By the end of 2025, AI might indeed function like a virtual team member at companies like Meta. But will it wipe out tech jobs entirely? More likely, it will reshape roles: some positions could diminish while new specialties arise. As with every wave of technological advancement, adaptability is key. Do you think AI will outright replace many mid-level engineering jobs, or will it primarily enhance productivity and shift the nature of work? #innovation #technology #future #management #startups

TL;DR: AI is changing software engineering jobs faster than most organizations are changing how they develop talent. Leaders need to act now, not later. The long version: AI dominated the business and technology conversation in 2025. Most of that debate focused on productivity. Far less attention has been paid to what AI means for people, skills and careers. That’s an area that needs much more attention in 2026. In this piece, Joanna Parke and I argue that the biggest risk isn’t whether AI makes teams faster today, but whether we are creating the talent, knowledge and cultures we’ll need in five years’ time. Ignore that, and you end up with skills gaps, weak succession planning and fragile engineering cultures. We’re already seeing a shift from writing code to orchestrating systems. Engineers are becoming stewards of reliability, performance and value, working alongside AI rather than competing with it. This is where the idea of the “expert generalist” really matters: people who deeply understand systems, fundamentals and failure modes, not just syntax. That makes how we grow talent critical. Junior engineers are increasingly AI-native, but foundational skills still matter. Leaders have a responsibility to ensure new developers learn how software really works, where AI helps, and where it doesn’t. Training alone isn’t enough; mentorship, pairing and collaborative learning are becoming strategic capabilities. We also explore whether it’s time to rethink apprenticeship models and professional standards in software engineering. That may sound radical, but AI is creating real questions of accountability and authority, and the industry needs an answer. This isn’t about resisting change. It’s about shaping it deliberately. If software has eaten the world, then how we develop the next generation of engineers is no longer a side issue. It’s a leadership issue. #AI #SoftwareEngineering #Leadership #Talent #FutureOfWork #TechnologyStrategy https://lnkd.in/eRGhnN7x