Hello friends, if you are a Software Engineer, Data Scientist, Machine Learning Engineer, or AI Engineer looking to level up your skills in 2027, you have come to the right place.

Today, I am going to share the best interactive courses on Educative to learn Generative AI, Python, System Design, Cloud Computing, JavaScript, React, and much more, based on my experience of trying more than 50 of them.

These courses are project-based and interactive, which makes for a better learning experience. When it comes to interactive courses, Educative.io is the first name that comes to my mind.

Why Educative?

Educative’s interactive, text-based platform is an excellent way to build expertise in Generative AI, Data Science, System Design, and more, with hands-on coding right in your browser.

I first came across Educative a couple of years ago when I was learning System Design and their Grokking the System Design Interview course caught my attention. At that time it was one of the only good System Design courses on the market, and Educative was among the first to offer a text-based interactive learning experience.

Since then I have not looked away. I still use Udemy and Coursera for video-based learning, but I also use Educative for text-based learning, because combining video with text is the best way to learn. Text-based learning is faster, and the interactive coding makes it more enjoyable.

On affordability, Educative has several plans, from Standard and Premium to Premium Plus. I went with the last one because I wanted to learn a lot, but you can pick whatever suits your needs.

Right now, Educative is offering a 50% discount, so you can get an annual or 2-year subscription for half the cost.

👉 Get 50% off Educative Unlimited

Educative Unlimited: Excel with AI-Powered Learning

Top 10 Educative Courses for Software Engineers in 2027

Here is my curated list of the 10 best Educative courses for software engineers, covering Gen AI, Data Science, Cloud, and other critical areas.

1. Grokking the System Design Interview

System design is a critical skill for senior software engineers. This course breaks complex concepts into digestible lessons and prepares you for technical interviews.

Key topics:

• Designing scalable systems
• Load balancing, caching, and databases
• Real-world system design case studies

Best for: Engineers preparing for system design interviews.

👉 Join Grokking the System Design Interview

Grokking System Design Interview: Patterns & Mock Interviews

2. Grokking the Generative AI System Design

This is a newer System Design course that focuses on Generative AI, which is quickly becoming a key feature of modern applications.

It teaches you to design generative AI systems with a practical, structured approach. You start with the foundations every software engineer should know, such as neural networks, transformers, tokenization, and embeddings. The course then introduces a 6-step SCALED framework for designing robust GenAI systems.

Through real-world case studies, you will design systems like:

• Text-to-text (e.g., ChatGPT)
• Text-to-image (e.g., Stable Diffusion)
• Text-to-speech (e.g., ElevenLabs)
• Text-to-video (e.g., SORA)

Best for: ML engineers, software engineers, AI enthusiasts, and managers who want to design, train, and deploy generative AI models.

👉 Join Grokking the Generative AI System Design

Grokking the Generative AI System Design Interview - AI-Powered Course

3. Generative AI Essentials

Generative AI is revolutionizing industries, and this learning path gives you the skills to build AI-powered applications.

Key topics:

• Fundamentals of Generative AI
• Building and fine-tuning LLMs (Large Language Models)
• GPT models and AI ethics
• Real-world applications of Generative AI

Best for: Developers interested in AI and machine learning.

👉 Join Generative AI Essentials

Generative AI Essentials - AI-Powered Course

4. Introduction to Data Science with Python

This course bridges the gap between software engineering and data science, teaching you to analyze data, build models, and deploy machine learning solutions.

Key topics:

• Python for data science, including pandas and Matplotlib
• Data visualization and analysis
• Machine learning basics

Best for: Software engineers transitioning into data science roles.

👉 Join Introduction to Data Science with Python

Introduction to Data Science with Python - AI-Powered Course

5. Clean Code in Python

Python is a must-know language for software engineers, data scientists, and AI developers. This course goes from Python basics to advanced topics so you can write efficient, clean code.

Key topics:

• Decorators, generators, and context managers
• Concurrency and parallelism
• Python for data science and AI

Best for: Developers looking to master Python.

👉 Join Clean Code in Python

Clean Code in Python - AI-Powered Course

6. Fundamentals of Machine Learning for Software Engineers

This learning path introduces software engineers to machine learning concepts, tools, and frameworks so you can build and deploy ML models.

Key topics:

• Supervised and unsupervised learning
• TensorFlow and PyTorch basics
• Model evaluation and deployment

Best for: Engineers exploring machine learning.

👉 Join Fundamentals of Machine Learning for Software Engineers

Fundamentals of Machine Learning for Software Engineers - AI-Powered Course

7. Building Full-Stack Web Applications With Node.js and React

Full-stack development is a highly sought-after skill. This course teaches you to build modern web applications with React on the front end and Node.js on the back end.

Key topics:

• React fundamentals and hooks
• RESTful APIs with Node.js
• Database integration (MongoDB)

Best for: Aspiring full-stack developers.

👉 Join Building Full-Stack Web Applications With Node.js and React

Building Full-Stack Web Applications With Node.js and React - AI-Powered Course

8. Mastering Kubernetes for Developers

Kubernetes is the backbone of modern cloud-native applications. This interactive course of roughly 20 hours teaches you to deploy, manage, and scale applications with Kubernetes.

Key topics:

• Kubernetes architecture and components
• Deploying and scaling applications
• Monitoring, troubleshooting, and security

Best for: Developers working with containerized applications.

👉 Join Mastering Kubernetes for Developers

A Practical Guide to Kubernetes - AI-Powered Course

9. Data Structures for Coding Interviews in Python

Data structures and algorithms are the foundation of software engineering. This course teaches you to implement and optimize them in Python, using strategies developed by FAANG engineers.

Key topics:

• Arrays, linked lists, and trees
• Sorting and searching algorithms
• Dynamic programming and recursion

Best for: Engineers preparing for coding interviews.

👉 Join Data Structures for Coding Interviews in Python

Data Structures for Coding Interviews - AI-Powered Course

10. Cloud Architecture: A Guide To Design & Architect Your Cloud

Cloud computing is a must-know skill for modern developers. This learning path teaches you to design and build cloud-native applications.

Key topics:

• Cloud infrastructure and services
• Serverless computing and microservices
• Cloud security and cost optimization

Best for: Developers transitioning to cloud-based development.

👉 Join Cloud Architecture: A Guide To Design & Architect Your Cloud

Cloud Architecture: A Guide to Design and Architect Your Cloud - AI-Powered Course

Why Choose Educative for Learning Tech Skills in 2027?

If you are still not sure whether Educative is right for you, here are a few reasons that helped me decide:

• Text-based learning: Learn at your own pace with interactive, text-based courses.
• Hands-on coding: Practice directly in the browser with embedded coding environments.
• Expert instructors: Courses are created by industry experts and seasoned developers.
• Career-focused: Many courses are designed to help you ace technical interviews and land your dream job.

They are also running a big discount right now, so if you want to level up your learning, now is the time to join.

👉 Get 50% off Educative Unlimited

Educative Unlimited: Excel with AI-Powered Learning

Final Thoughts

Whether you are diving into Generative AI, mastering Data Science, or sharpening your system design skills, Educative has a course for you. These 10 courses are a great starting point for software engineers who want to stay ahead in 2027.

Ready to level up? Check out these courses on Educative.io and start learning today.

Final tip: If you are preparing for tech interviews, start with Grokking the Coding Interview and Grokking System Design Interviews. These two courses alone justify the subscription cost.

Other Educative Resources You May Like

• Udemy vs Educative: Which Is Better in 2027?
• Top 6 System Design and API Design Interview Courses
• Is Grokking the Low Level Design Interview Using OOD Principles Worth It?
• Are Educative Courses Worth It for Developers?
• 11 Best Educative Courses for Coding Interviews and Development
• Is Educative.io Worth It in 2027? Review
• 5 Grokking Courses on Educative for Cracking Coding Interviews
• 5 Best Educative Courses to Learn Artificial Intelligence
• Is the Educative Unlimited Subscription Worth It?
• 5 Best Educative Courses to Learn Cloud Computing in 2027
• Is Grokking the Coding Interview Patterns in Java Worth It?
• 5 Best Educative Courses to Learn Generative AI and ChatGPT

Thank you for reading this article. All the best for your learning journey in 2027. If you have any questions, feel free to ask in the comments.

I Tried 50+ Educative Courses — Here Are My Top 10 Recommendations for Software Engineers was originally published in Javarevisited on Medium, where people are continuing the conversation by highlighting and responding to this story.

Management celebrated a 300% increase in pull requests. The engineer responsible for reviewing them had a different explanation for why nothing was getting shipped.

The first sign of trouble wasn’t a production outage. It was a pull request containing 1,847 changed lines, eleven new abstractions, and a comment from its author explaining that Claude had generated most of the implementation in fourteen minutes. Our engineering manager, Kevin, reacted with a fire emoji and congratulated the developer for embracing the future.

Our Staff Engineer, Nathan, reacted differently. He spent ninety minutes reviewing the change, discovered that its retry mechanism could execute the same financial operation twice, and requested a redesign. The developer replied that the AI had followed established distributed systems patterns and asked whether Nathan could provide a prompt that would produce a better implementation.

Nathan stared at the comment for a while. He’d spent thirteen years building production systems, survived enough database incidents to develop a personal dislike of Friday deployments, and somehow become the unpaid quality-control department for a machine that never attended incident reviews.

By the end of the month, his team had opened 412 pull requests. Kevin announced that engineering productivity had increased by more than 300%, using pull request volume as his primary evidence. Nathan had reviewed 186 of those changes and written less production code than at any point in his career.

Then he stopped approving pull requests that lacked a meaningful explanation from their authors.

Kevin called him a bottleneck.

Nathan called it the first accurate performance metric management had produced all quarter.

The Company Had Confused Code Generation With Software Delivery

Our company operated a subscription billing platform serving several thousand businesses. The backend consisted of Java and Spring Boot services, PostgreSQL databases, Kafka consumers, and enough billing exceptions to make accountants question whether software engineering was a legitimate profession.

The engineering team had recently adopted AI coding agents. Developers could generate endpoints, refactor services, create tests, and implement routine features much faster than before. Some improvements were genuine, particularly when engineers used the tools on well-understood tasks with clear acceptance criteria.

Kevin wanted to demonstrate the business impact. He created a dashboard tracking pull requests opened, lines changed, and average implementation time. Within weeks, the numbers looked extraordinary, and he began presenting them to executives as proof that AI had transformed the department.

Nathan asked why the dashboard didn’t include completed customer features, escaped defects, or the time reviewers spent understanding generated changes. Kevin said those metrics were harder to measure and that the company needed practical indicators of progress.

Nathan suggested that measuring engineering productivity through lines changed was roughly equivalent to measuring restaurant quality by the number of dishes washed. Kevin said he was being unnecessarily negative about innovation.

The disagreement became personal when Kevin announced that developers should aim to double their weekly pull request output. Nathan asked whether the company had also doubled its ability to review, test, and operate those changes. Kevin replied that experienced engineers needed to adapt instead of defending outdated workflows.

The Junior Developer Wasn’t the Problem

One of the team’s fastest developers was a twenty-six-year-old engineer named Alex. He used AI agents aggressively and could complete implementation tasks in hours that previously took several days. His code usually compiled, passed generated tests, and looked reasonably consistent with the existing architecture.

Alex wasn’t incompetent. He understood the product, asked good questions, and had already delivered several valuable features using AI assistance. But he had started trusting generated implementations before examining their failure behavior.

During a billing change, his agent introduced a retry mechanism that treated every timeout as a failed operation. Nathan explained that the external payment provider might have completed the request before the connection failed, leaving the system uncertain about the outcome.

Alex argued that the code included idempotency keys. Nathan pointed out that the key was regenerated on each retry, defeating the mechanism’s purpose. The generated tests passed because they mocked the provider to return clean success or failure responses, never an ambiguous outcome.

Alex looked embarrassed and admitted he hadn’t noticed. Nathan helped him redesign the workflow around a stable operation identifier and durable state tracking. The discussion took forty minutes, and Alex learned more about payment correctness than he had from the original implementation.

The following week, Alex submitted another large AI-generated change without reviewing its assumptions. Nathan asked him to explain the transaction boundaries, and Alex said he’d need time to investigate.

Nathan told him to take the time.

Kevin saw the delayed approval and sent Nathan a message asking why the team was struggling to maintain its new delivery velocity.

The Staff Engineer Went on a Review Strike

Nathan didn’t stop reviewing code entirely. He introduced a requirement that authors explain significant architectural decisions, describe relevant failure scenarios, and identify which parts of AI-generated changes they had personally verified. Routine changes still moved quickly, but complex pull requests could no longer arrive with a cheerful message claiming the tests were green.

Several developers appreciated the rule. Others complained that it removed the productivity advantage of AI because they now had to understand the code before submitting it.

Nathan considered that complaint particularly revealing.

Kevin called an emergency meeting after the average pull request review time increased. He accused Nathan of introducing unnecessary bureaucracy and argued that modern engineering teams couldn’t afford to inspect every implementation detail manually.

Nathan agreed that reviewing every line with equal attention was inefficient. He suggested using automated checks for routine concerns and concentrating human review on correctness, security, data integrity, and architectural risk.

Kevin asked why the team couldn’t rely on AI to review AI-generated code. Nathan said AI review was useful, but someone still needed to decide whether the system’s behavior matched the business requirements, especially when the requirements involved money moving between accounts.

Kevin said Nathan was making the process unnecessarily complicated. Nathan asked whether Kevin would personally accept responsibility for duplicate customer charges caused by an implementation nobody understood.

Kevin said that was an unfair question.

Nathan replied that production systems had a habit of asking unfair questions without scheduling a meeting first.

The CTO Asked for the Numbers Nobody Had Collected

The conflict reached our CTO, Rachel, after Kevin requested permission to change the code review policy. He argued that Nathan’s requirements were slowing AI adoption and preventing the company from realizing its productivity gains.

Rachel asked Kevin how many customer-facing features the team had delivered since adopting coding agents. He presented the pull request dashboard and emphasized the increase in engineering activity.

Rachel asked for completed features rather than pull requests. Kevin said the figures weren’t directly comparable because the team had changed its development process.

Nathan brought a separate report. It showed that pull request volume had increased sharply, while completed customer features had improved only modestly. Review queues had grown, and several complex changes required substantial rework after their initial submissions.

Rachel asked whether the AI tools were making developers faster. Nathan said they were, particularly for implementation work, but the organization had moved its bottleneck into review and validation without adjusting its workflow.

Kevin argued that the company needed more automated reviews. Nathan agreed that automation could help, then explained that the team also needed smaller changes, better specifications, and clearer ownership of generated code.

Rachel looked at Kevin and asked why his productivity dashboard hadn’t included review time. Kevin replied that he had focused on the most visible improvements.

Nathan said that was the problem. Management had measured the work AI made faster while ignoring the work AI made everyone else do.

The Experiment Nobody Expected Nathan to Support

Rachel proposed a four-week experiment. Half the engineering teams would continue using their existing AI-assisted workflows, while the others would adopt smaller pull requests, explicit acceptance criteria, risk-based review, and additional automated checks. Both groups could use the same AI tools.

Nathan supported the experiment immediately. Kevin seemed surprised because he’d assumed Nathan wanted to restrict AI adoption. Nathan explained that he wanted engineers to spend less time writing repetitive code, provided they remained capable of explaining and operating what they shipped.

The results weren’t dramatic enough for a conference keynote. Both groups benefited from AI assistance, but teams using smaller changes and clearer review expectations experienced less rework and more predictable delivery. Their advantage was most visible on tasks involving existing business rules and complicated system dependencies.

Alex became one of the strongest advocates for the revised process. He continued using coding agents, but started reviewing generated code before opening pull requests and writing tests around failure conditions the agents hadn’t considered. His raw pull request count declined, while the number of changes he completed without major rework improved.

Kevin eventually removed lines changed from his executive dashboard. He replaced the metric with completed work, review latency, and quality indicators, although he insisted the original dashboard had been intended only as an early experiment.

Nathan didn’t argue.

He was busy reviewing a pull request that contained a carefully documented retry mechanism, a reproducible test for an ambiguous payment outcome, and a short explanation of why the operation identifier remained stable across attempts.

The author was Alex.

Nathan approved it in twelve minutes.

The Engineer Who Refused to Become an AI Babysitter

During the next engineering meeting, Kevin announced that the company would continue investing in AI coding tools. He also acknowledged that generating more code hadn’t automatically translated into proportional improvements in delivered software.

Nathan agreed with the decision. He believed coding agents would become increasingly capable, and he had no interest in competing with a machine over who could type a repository class faster. His concern was that management might use the speed of generation to justify removing the people responsible for understanding the consequences.

A developer asked whether AI would eventually become good enough to review its own work reliably. Nathan said he expected automated verification to improve substantially, particularly for well-specified behavior and mechanically checkable properties. He added that someone would still need to establish the requirements, evaluate the remaining risks, and decide what level of evidence was sufficient.

Kevin asked whether that meant Staff Engineers would remain necessary forever. Nathan said he had no idea which job titles would survive, and anyone claiming certainty about the next decade was probably selling something.

Then he pointed toward the production dashboard. The company had processed millions of billing operations that month, and customers expected every transaction to be handled correctly regardless of whether the implementation came from a developer, a coding agent, or an unusually talented refrigerator.

Kevin laughed despite himself.

Nathan opened the next pull request. It was generated with AI, contained fewer than two hundred changed lines, and included an explanation of the business invariant the implementation needed to preserve.

He read the explanation first.

For the first time in months, reviewing AI-generated code felt like engineering again.

Practice the Decisions That Separate Senior Engineers From Code Generators

AI can accelerate implementation, but Senior and Staff Engineers still need to reason about correctness, distributed failures, scalability, and system trade-offs. Those skills become especially visible during system design interviews, where a convincing diagram means little if the candidate cannot explain what happens when its assumptions fail.

I’m building an interactive AI-powered system design interview platform through ProdRescue AI to help engineers practice realistic architecture scenarios and receive detailed feedback on their decisions. If you want to help shape the platform and request private beta access, you can complete the System Design Interview Platform Research Form.

You can also download my free Senior Engineer Interview & Production Playbook for practical material on system design, backend engineering, and production debugging. I write more about production debugging, system design interviews, AI, and the uncomfortable realities of engineering careers on Substack, where you can subscribe for future articles.

AI-generated code review, AI coding agents, developer productivity, Claude Code, Staff Engineer, software engineering productivity, AI code quality, pull request review, AI-assisted development, senior software engineer.

Our Best Engineer Stopped Reviewing AI-Generated Code. The Team Called Him Lazy was originally published in Javarevisited on Medium, where people are continuing the conversation by highlighting and responding to this story.

The board wanted a leaner engineering organization. Six months later, the company was spending more on contractors than it had saved in salaries.

The announcement arrived on a Wednesday morning, seventeen minutes before the engineering all-hands meeting. Our CEO, Richard, explained that advances in AI coding tools had fundamentally changed how software companies should operate. The company would reduce its engineering workforce by 30%, invest heavily in AI-assisted development, and become significantly more efficient.

He called it a strategic transformation. The finance department called it an $8.4 million annual reduction in projected payroll expenses. The engineers who lost their jobs had considerably less enthusiasm for either description.

Our Engineering Director, Laura, sat through the presentation without speaking. She’d spent the previous month explaining that the company’s biggest delivery problems involved unclear product requirements, fragile integrations, and production systems nobody fully understood. Richard had acknowledged her concerns before approving a plan that treated engineering headcount as the primary constraint.

After the meeting, Laura received a message from her strongest Staff Engineer, Ben. He wanted to know whether the company expected the remaining engineers to maintain the same systems while delivering the same roadmap. Laura answered that leadership was still revising its expectations.

Ben replied that the systems weren’t waiting for the revision.

The Spreadsheet Made Perfect Sense

The company’s financial model assumed that AI coding assistants would increase developer output enough to compensate for the smaller engineering team. Executives had seen demonstrations where engineers generated working applications in minutes and completed routine development tasks dramatically faster.

Richard believed those improvements could be applied across the organization. He’d already purchased enterprise AI licenses and expected every engineer to use them for implementation, debugging, and testing.

Laura supported the tools. Her team had used AI assistants to generate boilerplate, investigate unfamiliar code, and accelerate certain refactoring tasks. Several engineers reported meaningful time savings, especially when working on well-defined changes with clear acceptance criteria.

The problem was that writing code represented only part of their workload.

The company operated a twelve-year-old enterprise platform with hundreds of customer-specific integrations. Engineers spent substantial time understanding undocumented behavior, coordinating database changes, investigating production incidents, and validating requirements that frequently contradicted earlier agreements.

AI could help with portions of that work. It couldn’t make an external banking provider respond faster, eliminate an ambiguous customer contract, or automatically establish who was authorized to approve a risky migration.

Laura explained this during the restructuring discussions. Richard asked whether she was underestimating how quickly the technology was improving.

She said she was estimating the work the company actually needed to perform.

Richard approved the layoffs anyway.

The First Six Weeks Looked Fantastic

Engineering output initially improved.

The remaining developers completed more pull requests per person, and several teams used AI agents to accelerate routine implementation. Richard shared the numbers during a board meeting and described the restructuring as evidence that smaller AI-enabled teams could outperform larger traditional organizations.

The dashboard looked impressive because it measured activity against a reduced headcount. It didn’t show that several experienced engineers had left behind unfinished migrations, undocumented operational knowledge, and customer relationships that required technical continuity.

Ben inherited responsibility for three services after their original owners were laid off. He also became the escalation point for an authentication system he hadn’t worked on in four years.

His team used AI tools aggressively. They generated tests, explored unfamiliar code paths, and drafted implementation changes faster than before.

But every significant production change still required someone to understand the consequences.

The AI assistant could suggest a database migration in thirty seconds. Determining whether the migration would block writes on a table containing hundreds of millions of records took considerably longer.

By the end of the sixth week, the team had produced more code and completed fewer major customer commitments.

Richard asked Laura why the roadmap was slipping.

She explained that the company had reduced its capacity to review, integrate, and safely operate the software it was generating.

Then the Customer Escalations Started

The first serious problem involved a major enterprise customer whose nightly data synchronization began failing intermittently. The integration had been maintained by a senior engineer who’d been included in the layoffs.

The remaining team found documentation describing the expected workflow, but several recovery procedures depended on assumptions that were no longer accurate. An AI assistant identified a likely retry issue and proposed a reasonable code change.

Ben reviewed the suggestion and discovered that the retry mechanism could duplicate downstream operations when the external provider returned an uncertain outcome.

He rejected the change.

The investigation continued for three days. The customer escalated the issue to the account director, who demanded an immediate resolution because the failures were affecting financial reporting.

Richard asked why the team couldn’t use AI to resolve the incident faster. Ben explained that the assistant had been useful for identifying relevant code, but the difficult part was establishing the correct behavior across systems with incomplete information.

Richard suggested bringing in a specialist contractor.

Laura reminded him that the company had employed a specialist six weeks earlier.

The Contractors Arrived With Better Rates Than the Engineers

By the third month, the company had hired outside consultants to support several delayed projects. Their hourly rates were substantially higher than the effective hourly cost of the engineers who had previously owned those systems.

Finance approved the spending because the contracts were temporary. Laura asked whether anyone had calculated how much time the remaining engineers spent onboarding the consultants.

The answer was no.

Ben spent two weeks explaining the architecture to a contractor assigned to a critical data migration. The contractor was competent and experienced, but he needed context that had disappeared with the original team.

Meanwhile, Ben’s own work stopped moving.

Richard began asking why engineering productivity had declined despite the company’s investment in AI. Laura showed him the difference between pull request volume and completed customer outcomes.

The team was producing implementation changes quickly. Those changes accumulated in review queues, integration environments, and release schedules because the remaining engineers were responsible for too many systems.

Richard argued that the organization needed better processes.

Laura agreed.

She also said that processes couldn’t create experienced engineers out of missing headcount.

The Best Engineers Started Leaving

Ben received a recruiting message from a former colleague offering him a Staff Engineer role at another company. He hadn’t been actively looking, but the position promised ownership of a defined platform and a team with enough engineers to maintain it.

He scheduled an interview.

Two other senior engineers began interviewing that month. Neither had been directly threatened with redundancy, but both were exhausted from carrying the operational responsibilities of teams that no longer existed.

Laura discovered the situation when one engineer requested a reference. She asked why he was leaving, and he explained that the workload had become unpredictable while leadership continued describing the restructuring as a success.

He wasn’t opposed to AI.

He used coding assistants every day and considered them useful. What frustrated him was the assumption that faster code generation automatically reduced the need for experienced people who understood the systems.

Laura brought the retention risk to Richard. He suggested offering bonuses to critical engineers.

She asked why the company was suddenly willing to pay premiums to retain people whose roles it had recently described as increasingly replaceable.

Richard said the market was competitive.

Laura said it had been competitive before the layoffs too.

The Board Asked for the Real Numbers

At the six-month review, Richard presented the restructuring results. Payroll expenses had fallen, and AI adoption had increased across engineering.

Then the CFO presented the broader financial picture.

Contractor spending had risen sharply. Several customer commitments had been delayed, incident-related overtime had increased, and the company was paying retention bonuses to engineers considered essential to production operations.

The original savings projection had assumed that most engineering responsibilities would disappear or become significantly cheaper. In practice, much of the work had been redistributed, deferred, or purchased from external specialists.

The board asked whether the restructuring had improved operating margins.

The CFO said the answer depended on which costs were included, but the available results were substantially weaker than the original forecast.

Richard argued that the company was still adapting to the new operating model. Laura agreed that organizational changes required time, but pointed out that the company had reduced critical expertise before establishing which responsibilities AI could reliably absorb.

One board member asked whether the remaining engineers were more productive.

Laura said they were writing code faster in several areas. She couldn’t honestly say the organization was delivering customer value faster overall.

The distinction changed the conversation.

The Company Reversed Course. The Engineers Didn’t.

Richard eventually approved selective hiring for critical backend and platform roles. The company also introduced clearer ownership boundaries and began measuring delivery outcomes rather than relying primarily on coding activity.

The AI tools remained in place.

Engineers continued using them because the productivity improvements were real in suitable tasks. The organization simply stopped assuming that every minute saved during implementation translated into an equivalent reduction in staffing requirements.

Ben resigned before the new hires arrived.

Richard offered him a substantial retention package and a larger technical leadership role. Ben declined, explaining that he’d spent months trying to protect systems while the organization treated operational expertise as an expense it could remove without consequences.

Laura understood his decision.

She stayed long enough to rebuild the teams and establish more realistic capacity planning. During the next annual review, she presented a new engineering productivity framework focused on customer outcomes, operational reliability, and the cost of maintaining software over time.

Richard approved it.

He had become noticeably less interested in counting pull requests.

A year after the layoffs, the company employed fewer engineers than it had before the restructuring, but more than the original plan had anticipated. Some AI-driven efficiency gains had endured, while several functions required the experienced staff leadership had initially hoped to eliminate.

The restructuring wasn’t a complete failure. It had exposed inefficient processes and forced the organization to improve how it used engineering resources.

It had also demonstrated how expensive a confident assumption could become when leadership treated a technology’s potential as proof that its organizational benefits had already been realized.

Ben heard about the changes from Laura several months later. He was pleased the company had improved, although he had no interest in returning.

His new employer also used AI coding agents.

The difference was that nobody expected an autocomplete system to take responsibility for a production outage at three in the morning.

Build the Skills That Still Matter When AI Writes the Code

AI coding assistants are changing how engineers implement software, but Senior and Staff roles still demand judgment about system architecture, failure recovery, scalability, and operational trade-offs. Understanding these areas helps engineers make better decisions when AI-generated solutions appear convincing but overlook important production constraints.

I’m building an interactive AI-powered system design interview platform through ProdRescue AI to help engineers practice realistic architecture scenarios and receive structured feedback on their technical reasoning. You can contribute to the product research and express interest in private beta access through Help Us Build the System Design Tool You Actually Need.

You can also download my free Senior Engineer Interview & Production Playbook for practical guidance on backend engineering, production debugging, and system design. I write about production debugging, system design interviews, AI, and engineering careers on Substack, where you can subscribe for future articles.

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Artificial Intelligence, Software Engineering, Layoffs, Engineering Leadership, Workplace Culture

Our CEO Fired 30% of the Engineers Because of AI. The Remaining Team Started Interviewing was originally published in Javarevisited on Medium, where people are continuing the conversation by highlighting and responding to this story.