Shadowing Practice: Why Companies Are Quietly Rehiring Software Engineers - Learn English Speaking with Video
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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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Woordenschat en spreektips bij deze les
Deze spreekles op niveau B2 is gebaseerd op de video “Why Companies Are Quietly Rehiring Software Engineers”. Deze woorden komen het vaakst terug: code, developer, companies, generated, software. Deze video bevat 132 zinnen en 1340 woorden om na te spreken. Het gesproken deel duurt 9:00. De spreker praat in een gelijkmatig tempo van ongeveer 149 woorden per minuut, prettig om te shadowen. Slechts 78% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden, dus de woordenschat is pittig.
Belangrijke woorden in deze video
De 15 moeilijkste woorden uit de video, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| developer zelfstandig naamwoord | /dɪˈvɛləpɚ/ | ontwikkelaar, ontwerper |
| productivity zelfstandig naamwoord | /ˌpɹɒdʌkˈtɪvəti/ | vruchtbaar |
| limitation zelfstandig naamwoord | /lɪmɪˈteɪʃən/ | begrenzing, beperking |
| boomerang zelfstandig naamwoord | /ˈbuːməɹæŋ/ | boemerang |
| optimize werkwoord | /ˈɑptɪmaɪz/ | optimistisch zijn/handelen |
| uneven bijvoeglijk naamwoord | /ʌnˈivən/ | oneven |
| reconsider werkwoord | /ˈɹiːkənˌsɪdə/ | heroverwegen |
| supervision zelfstandig naamwoord | /ˌs(j)uːpəˈvɪʒən/ | supervisie, toezicht |
| detect werkwoord | /dɪˈtɛkt/ | detecteren |
| bottleneck zelfstandig naamwoord | flessenhals | |
| catchy bijvoeglijk naamwoord | /ˈkæt͡ʃi/ | aanstekelijk, pakkend |
| constraint zelfstandig naamwoord | /kənˈstɹeɪnt/ | beperking, inperking |
| outsourcing zelfstandig naamwoord | /ˈaʊtˌsɔː(ɹ).sɪŋ/ | uitbesteding |
| supervise werkwoord | /ˈsuː.pə.vaɪz/ | toezien, besturen |
| tirelessly bijwoord | onophoudelijk, onvermoeibaar |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| lay off werkwoord | ontslaan, de zak geven |
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | has made · have laid · have priced |
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | would be replaced · is estimated · being made |
Uitspraak om op te letten
De spreker gebruikt 6 samentrekkingen en verkorte vormen, zoals they're, you're. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: algorithm /ˈælɡəɹɪðm̩/, altogether /ɔl.tuˈɡɛð.ɚ/
- De klanken “sh” en “zh”: limitation /lɪmɪˈteɪʃən/, supervision /ˌs(j)uːpəˈvɪʒən/, reshape /ɹiːˈʃeɪ̯p/, corrections /kəˈɹɛkʃənz/, valuation /ˌvæ.ljuːˈeɪ.ʃən/
- Lange woorden — let op de klemtoon: developer /dɪˈvɛləpɚ/, productivity /ˌpɹɒdʌkˈtɪvəti/, limitation /lɪmɪˈteɪʃən/, reconsider /ˈɹiːkənˌsɪdə/, supervision /ˌs(j)uːpəˈvɪʒən/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: optimize /ˈɑptɪmaɪz/, supervise /ˈsuː.pə.vaɪz/, corrections /kəˈɹɛkʃənz/, freeze /ˈfɹiːz/, depend /dɪˈpɛnd/
- /æ/ — opener dan de Nederlandse “e”: boomerang /ˈbuːməɹæŋ/, catchy /ˈkæt͡ʃi/, havoc /ˈhævək/, syntax /ˈsɪn.tæks/, compatibility /kəmˌpætəˈbɪlɪti/
- /g/ — een harde plofklank, geen Nederlandse “g”: integrate /ˈɪn.tɪ.ɡɹeɪt/, beginner /bəˈɡɪnɚ/, delegate /ˈdɛlɪɡət/, algorithm /ˈælɡəɹɪðm̩/, altogether /ɔl.tuˈɡɛð.ɚ/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Spreek zin voor zin na op normale snelheid en herhaal elke zin tot je ritme gelijk is aan dat van de spreker.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als developer, productivity, limitation.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.