Pratique du Shadowing: Why Companies Are Quietly Rehiring Software Engineers - Apprendre l'anglais à l'oral avec YouTube
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By 2030, 90% of developers would be replaced by AI.
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By 2030, 90% of developers would be replaced by AI.
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Those were the experts' estimates a few years ago,
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but today, the reality is very different.
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Gartner just said that 50% of companies who laid off workers
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because of AI will rehire for the exact same roles by 2027.
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So far in 2026, software engineer hiring is skyrocketing.
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It's true that it could wreak havoc,
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but I think it's much more likely to reshape jobs rather than to erase them altogether.
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But this is not necessarily because companies are not using AI,
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but because AI makes so many mistakes when writing code,
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that expert developers have to correct it 9 out of 10 times.
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Tech giants like Microsoft and Google are outsourcing more and more coding to AI in a productivity push,
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but some new research shows the tools might not be as helpful as some expect.
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This not only affects developers' productivity, but also companies' finances since the increase in AI generated junk code raises the workload.
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Seasoned engineers were actually 19% slower when using AI tools.
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Instead of speeding these engineers up,
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the AI often gave them suggestions that looked helpful,
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but actually required time-consuming corrections.
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All of this has made companies reconsider their decision to delegate code creation entirely to AI,
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because instead of reducing costs as previously thought,
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it is now increasing them.
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So, why are companies quietly rehiring software engineers?
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Over the past few years,
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the tech industry has repeatedly told us something that seemed inevitable.
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Companies like Google, Amazon, Meta,
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and many others have tirelessly repeated one phrase.
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Artificial intelligence is going to replace workers, especially developers.
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Since early 2024, companies began reducing teams,
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freezing hiring, and betting on automatic code generation tools that promise to build complete software in seconds.
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It is estimated that since 2024,
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companies have laid off around 124,000 software developers.
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Amazon, Chegg, Microsoft, Meta, Salesforce, the list continues.
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Tech companies just are cutting tens of thousands of jobs.
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The idea seemed simple.
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Fewer developers, more automation, and higher profit margins for companies.
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However, what started as an apparently unstoppable revolution quickly began to show its limitations.
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News such as Amazon experiencing four critical errors in just 90
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days due to AI-assisted code changes made the world realize that AI was not as promising as believed.
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Reality began to show that writing code is not the same as developing software and that artificial intelligence,
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although powerful, still heavily depends on human supervision.
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Gradually, companies began to notice that reducing teams was not generating the expected results,
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but instead creating new problems,
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more complex and costly to solve.
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The study suggests that the return on AI coding,
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it may be more uneven,
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less immediate than investors have priced it.
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While AI proved to be extremely efficient at generating code quickly,
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the truth is that it also began producing large volumes of code that required constant correction.
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Recent studies have shown that AI generated code contain up to 1.7 times more errors than code written by humans.
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This has forced companies to spend more time reviewing and fixing these issues.
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Additionally, massive code generation has caused companies to have up to 38% more code to maintain,
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increasing system complexity.
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The problem is that software development is no longer what it used to be,
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because now the challenge is no longer writing code,
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but maintaining it, optimizing it and fixing it.
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A relevant fact is that 61% of companies that adopted AI tools for programming increased their hiring of senior developers in 2026,
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mainly to review AI-generated code.
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Now some of those mid-level Google engineers are shaking free.
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This is your dream.
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So you just need to hold your hand up in this job environment
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if you're a former Google engineer and you're going to get offers.
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Another major problem is that artificial intelligence generates code without understanding business context.
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According to Gartner, more than 50% of errors in AI-generated code are related to a lack of business context understanding,
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not syntax or programming errors.
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AI can write functions, structures,
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and algorithms based on learned patterns but it does not understand business objectives,
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technical constraints or strategic decisions.
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This means that many times the generated code works in isolation but fails when integrated into real systems.
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This has been notable as a study by IBM found
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that 4 out of 10 development teams reported compatibility issues when integrating AI-generated code into existing infrastructure existing infrastructures.
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As a result, human developers must intervene to adapt that code,
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fix errors, and optimize performance,
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increasing the need for experienced developers.
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The tools, they're certainly being used,
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they're here, the payoff may just be more uneven than the hype suggests and perhaps it plateaus at a certain point.
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But perhaps the biggest problem with AI is that,
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unlike a human developer, AI cannot correct itself.
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When a developer makes a mistake,
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they can realize it in time.
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But when AI makes a mistake,
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it does not detect it unless you point it out.
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Researchers at Princeton University discovered that AI models failed to self-correct in more than 60% of cases,
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even when asked to review their own code.
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This has led companies like Google,
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Amazon and Meta to begin reconsidering their initial strategy.
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Companies are trying these tools,
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they're not always working, and so they're asking eventually, where's the value?
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And experienced developers are aware of AI's limitations.
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Recent studies have indicated that up to 96% of developers do not fully trust AI-generated code.
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This has turned code supervision into one of their central tasks,
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leaving aside important tasks such as innovation.
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As a consequence, the real productivity of the few remaining developers began to decline.
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A GitHub study found that 49% of teams reported a decrease in real productivity.
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And ultimately, this translates into higher costs for companies.
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Big tech now pouring more capital than ever into AI.
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Alphabet, Microsoft, Meta, Amazon expected to spend a combined $600 billion in capex.
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That's this year alone.
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All these limitations of AI-generated code have created a bottleneck in development teams.
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As a result, companies have begun rehiring employees who were previously laid off.
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It is estimated
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that up to 4 out of 10 new hires are software developers who were former employees of companies
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that have laid them off after replacing them with AI.
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This phenomenon, known as boomerang hiring,
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is rapidly growing in the tech sector,
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as these professionals can integrate faster,
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understand internal systems, and detect complex errors that AI fails to identify.
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I want you back.
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It's a catchy tune.
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And it's also an increasingly popular hiring trend.
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It's what's called boomerang hire.
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35% of new hires being made up of past employees.
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While it is true that AI is replacing some software developer roles,
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these are mostly junior positions,
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since AI can replace the work of a novice developer,
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but not that of an experienced one.
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programming tasks traditionally assigned to beginner developers can now be performed by AI tools.
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This has caused many companies to reduce junior hiring and increase demand for experienced developers.
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More than 54% of companies have indicated they plan to hire more senior developers while reducing junior positions,
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reflecting a structural shift in the tech industry.
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Now this latest suggests that while it can help that group level up,
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It may actually be increasing reliance on senior talent because someone still needs to debug,
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refine and ship the final product.
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The tech industry is beginning to recognize that artificial intelligence does not replace developers on its own.
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And this is reflected in recent hiring trends.
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For example, around 20% of the software engineers hired by Google in 2025 were former employees who were rehired.
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But this return is happening quietly,
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without major announcements, yet with a significant impact on the labor market.
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The developers being rehired are those with experience,
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capable of supervising artificial intelligence and improving generated code.
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This demonstrates that artificial intelligence is not at the level of an experienced developer.
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Sometimes it is simply used as an excuse to boost stock valuations and lay off workers.
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Let us know what you think in the comments below.
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About This Lesson
In this lesson, you will practice your English speaking skills by exploring the key themes surrounding the trend of companies rehiring software engineers in response to the challenges posed by artificial intelligence. By engaging with the content of the video titled "Why Companies Are Quietly Rehiring Software Engineers," you will develop your listening comprehension and improve your ability to discuss relevant topics in a professional context. This exercise will help enhance your vocabulary and refine your pronunciation, making it an excellent opportunity for those preparing for IELTS speaking practice.
Key Vocabulary & Phrases
- AI (Artificial Intelligence) - Machines programmed to perform tasks that typically require human intelligence.
- Code - The written instructions that make up software programs.
- Outsource - To hire external resources or services to perform tasks, usually to save costs.
- Productivity - The efficiency of production; the rate at which goods or services are produced.
- Errors - Mistakes that occur in code or software affecting functionality.
- Developers - Professionals who create software applications and systems.
- Supervision - The act of overseeing or managing a process, in this case, the development of code.
- Rehiring - The process of bringing back previously laid-off employees to the workforce.
Practice Tips
To make the most of this lesson, follow these shadowing tips that align with the speed and tone of the YouTube video:
- Start by watching the video without sound, visualizing the key points based on the visuals. This will set a context when you listen actively.
- Play the video and listen carefully, noting the emphasis on phrases where the speaker expresses opinions about AI and its impact. Use a shadow speech technique by repeating phrases immediately after hearing them to improve fluency.
- Practice slowing down the video playback to follow the speech more closely. This will enable you to catch key vocabulary more easily while allowing for clearer pronunciation practice.
- Focus on the intonation and rhythm of the speaker's voice. Notice how emotions are conveyed through pitch changes – this is essential for effective communication.
- Record your shadowing exercises and review your pronunciation and fluency. Identifying areas for improvement can highlight where you might need further IELTS speaking practice.
By incorporating these methods into your learning routine, you will not only enhance your speaking skills but also gain valuable insights from industry-specific discussions. This insightful practice is an excellent way to learn English with YouTube while engaging in a shadowing site focused on real-world content.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.
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