Практика Shadowing: Why Companies Are Quietly Rehiring Software Engineers - Изучайте разговорный английский с 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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В этом уроке вы будете практиковать английский язык с акцентом на темах, связанных с увольнением и повторным наймом программистов. Вы узнаете, как искусственный интеллект влияет на эту сферу, а также попрактикуетесь в речевых конструкциях и словах, которые часто используются в технологической индустрии. Особое внимание будет уделено улучшению произношения английского через метод shadow speech.
Ключевая лексика и фразы
- software engineers - программисты
- AI (искусственный интеллект) - искусственный интеллект
- code generation - генерация кода
- productivity - продуктивность
- errors - ошибки
- productivity push - увеличение продуктивности
- costs - затраты
- development - разработка
Советы по практике
Для того чтобы максимально эффективно использовать shadow speak в этом уроке, постарайтесь следовать нескольким простым рекомендациям:
- Слушайте внимательно: Перед тем как повторять фразы, послушайте каждое предложение несколько раз. Обратите внимание на интонацию и ударение.
- Чередуйте скорость: Если материал кажется слишком быстрым, замедляйте воспроизведение. Это поможет вам лучше уловить мелкие детали произношения. Вы можете попробовать использовать замедленный режим воспроизведения, чтобы улучшить произношение английского.
- Повторяйте вслух: Каждый раз, когда вы слышите новую фразу, остановите видео и повторите её. Использование метода shadowspeaks поможет вам закрепить материал.
- Записывайте свое произношение: Сравните своё произношение с оригиналом. Это поможет вам выявить области, требующие улучшения.
- Обсуждайте с друзьями: Практикуйте разговорный английский с кем-то еще. Обмен мнениями о содержании видео поможет закрепить изученные фразы и улучшить вашу уверенность в разговоре.
Данный урок призван помочь вам не только улучшить разговорные навыки, но и адаптироваться к современным изменениям в сфере технологий, что является важной частью вашего обучения.
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.
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