Pratica di Shadowing: How AI Became More Expensive Than The Workers It Replaced - Impara a parlare inglese con i video
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In 2022, ChatGPT was released to the market.
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And since then, the growth of AI has been impressive.
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The creation of hyper -realistic images, code generation, and even the delegation of complex tasks.
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AI seemed capable of doing everything with great ease.
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And after seeing everything it was capable of, companies began adopting AI exponentially.
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They incorporated it into their processes and realized that it was extremely cheap.
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Bye!
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Precisely because of its low costs, AI also began to bring problems.
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"AI company called Anthropic is raising alarms tonight about AI's potential impact on employment that could soon be felt." Thousands of layoffs,
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team reductions, and massive displacement.
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Literally, AI was eliminating jobs across every industry.
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from roles in tech companies, to fast food chains, to fast food chains, AI was wiping out almost any type of job.
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It could perform the work of entire teams in just minutes, largely because it was much more efficient, but above all because it was cheaper to maintain.
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In recent months, a problem has started to emerge.
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AI is becoming very expensive.
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Because AI, maybe, is becoming too expensive to scale.
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Using open claw these days is like driving a Ferrari.
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So much so that Microsoft itself banned its own employees from using clawed code due to its high costs.
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So Microsoft starts canceling clawed code licenses.
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But this is not only happening at Microsoft.
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Uber.
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NVIDIA.
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Amazon and hundreds of other companies have reported similar cost problems, making it almost impossible even for top executives to justify such a high expense.
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Big tech is scrambling to slow down its use of AI.
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Major companies have already exhausted their annual budgets And some say it's getting more difficult to justify the costs.
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But if AI promised to be so cheap and efficient, How did it end up costing more than the workers it replaced?
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Although the birth of artificial intelligence dates back to the beginning of the 1950s, it was not until 2022 that this technology truly experienced a boom.
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It promised efficiency and solutions to complex problems with just one click.
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But most importantly, it promised to save companies a lot of money.
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From technology companies to fast food chains, every business began implementing it at an unprecedented speed.
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It was such a revolutionary technology that it could do the work of 10 people at one tenth of the price.
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Or, at least, That was what people were saying.
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"AI is a technology.
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for taking away your job and maybe eventually displacing human sovereignty over our own future.
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Starting in 2023, companies began not only encouraging, but also forcing their employees to use AI tools, causing demand for these models to grow uncontrollably.
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And the way companies measured their AI performance and usage was through tokens.
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Using a high number of tokens became somewhat of a status symbol among employees.
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proving that they were going above and beyond to meet key performance indicators.
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But there was a problem.
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After realizing this, employees began using these tokens successively simply to appear more efficient at the end of the month.
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This gave rise to what became known as token maxing.
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"Token max?
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Like, that's actually the coolest thing you can do now." "Token maxing
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and this new way of building." But token maxing is expensive.
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For text -based inquiries, the math is simple, with 750 words costing 1 ,000 tokens.
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And generating videos, images, code, and more can cost much more.
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By the end of 2025, token maxing began becoming popular among tech companies,
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making it increasingly common for software engineers to use AI even for insignificant tasks.
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But much of this popularity was driven by statements like these: "That $500 ,000 engineer, at the end of the year, I'm gonna ask him how much did you spend in tokens,
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and that person said $5 ,000, I will go ape something else." Yes.
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$250 ,000 worth of tokens, I am going to be deeply alarmed." Interestingly,
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NVIDIA's CEO, one of the companies benefiting the most from AI adoption, strongly encouraged all developers to use these tokens.
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The problem is that all this demand did not correspond to real demand but rather was artificially inflated.
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Believe it or not, Silicon Valley engineers are now being scored on how many AI tokens they use.
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The problem: that may end up inflating overall AI demand." At the same time,
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Shortages of electronic components needed for building data centers are causing many of these projects to be delayed.
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According to Bloomberg, nearly 50 % of all data center construction projects planned for 2026 in the US have been canceled
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or delayed but many of these constructions and plans were based on the enormous demand that AI companies were receiving.
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According to the Economic Times, demand for artificial intelligence infrastructure continues to greatly exceed supply.
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The thing is
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that experts are now beginning to question how much of this demand actually comes from real usage
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and how much comes from inflated figures.
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So when a hyperscaler like Andy Jassy at Amazon in his annual letter this morning
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or that deal this morning tells Wall Street that demand is outstripping supply,
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you gotta ask how much of that is real enterprise adoption and how much is engineers juicing a leaderboard?
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But due to token maxing, the cancellation of data center construction projects, and the massive adoption of AI worldwide, the cost of those tokens has increased significantly.
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"Aught of concerns over the rising cost of artificial intelligence, it led to a major tech stock sell -off that dragged down the S &P and NASDAQ,
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which fell more than 500 points." According to Bloomberg Finance, The average cost of tokens from large language models, or LLMs, has more than doubled since 2025.
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In December of that year, the average cost was $1 .01 per million tokens,
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and by May 2026, that number had reached $2 .12 per million tokens.
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While this increase in price may seem insignificant, when taking into account the massive amount of tokens these companies use every day,
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it translates into tens of millions of dollars per month.
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use about $200 worth of tokens every week.
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50 weeks a year, that's 10 ,000 bucks in tokens.
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You have 40 ,000 employees, that's $400 million.
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You have 90 ,000 employees, that's $900 million.
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AT &T, for example, reported that it was consuming nearly 8 billion tokens every single day.
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Without a doubt, this has been incredibly profitable for Anthropic and OpenAI, the leading AI companies in the world today.
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According to Reuters, thanks to the adoption of AI agents at the enterprise level, Anthropic aims to nearly triple its annualized revenue in 2026.
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To put that number into perspective, In 2025, Anthropic ended the year with revenue of around $3 billion,
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and in 2026, that figure is expected to reach nearly $9 .6 billion.
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But as a result, employers are frequently blowing through their AI budgets, putting them in a situation where it's no longer clear that using AI is cheaper than hiring people.
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And AI companies are beginning to realize this.
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The AI bill has come due.
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Annual budgets blown through in weeks.
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And now companies are facing a choice nobody has ever had to make before.
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tokens or humans.
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An internal memo from a Microsoft executive vice president instructed engineers to stop using Anthropics AI coding tools.
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At first, Executives said it was to encourage the use of AI tools within the same ecosystem.
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such as GitHub Copilot, But later it was discovered
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that the main reason was the enormous costs generated by the use of Claude code.
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And Microsoft is not alone in this, as Fortune reports that other companies are also pulling back on AI usage.
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For example, Reuters reported that Meta consumed nearly 60 trillion tokens in a single month.
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That's roughly 900 million dollars worth of Anthropix tokens based on the firm's blended prices.
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If tech now costs what humans cost, every company in America is about to start asking which one to keep.
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But these extra costs are becoming a real headache for corporate finance departments.
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In fact, A Gartner survey found that three -quarters of the executives it polled expect technology budgets to increase this year,
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and nearly half estimate double -digit percentage increases.
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A sizeable chunk of that spending will go toward AI, which is expected to account for more than one -fifth of total enterprise technology spending by 2035.
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And the numbers prove it.
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Global spending on AI agents and models is rising at concerning rates.
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By 2027, spending on this technology is estimated to reach $680 billion, more than double what companies spent in 2025.
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But these estimates were made using the current average costs of these tools, and considering that both and OpenAI are still not profitable.
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they are operating at a loss.
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But that is about to change because in the coming months, both companies are expected to go public and will have to answer to shareholders.
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Therefore, they will inevitably increase the prices of using tokens from their AI tools and agents in order to generate profits.
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In other words, the amount of money companies are currently paying for tokens will multiply exponentially.
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So, if the cost of using these tools continues to rise, of tokens.
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The thing is, This is not a prediction.
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In fact, this is already happening.
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Maybe human labor will be more cost efficient after all.
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Uber and Microsoft are starting to second -guess aggressive AI adoption.
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That's partly because the cost of using AI can exceed the cost of human employees.
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According to Reuters, the current cost of running AI agents is already comparable to the cost of human labor
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and in some cases, it is already more expensive.
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While in coding tasks, the use of AI is undeniably cheaper.
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In data entry tasks, the difference is minimal.
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But in others, such as call centers, the cost of human labor is actually cheaper.
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Although AI companies have so far been able to subsidize the real cost of tokens due to generous and abundant funding, that appears to be coming to an end.
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According to the Wall Street Journal, enterprise customers are beginning to question the true return on these investments.
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And the reality is that with the public listings of Anthropic and OpenAI, combined with investor pressure to see returns and electronic component shortages,
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this creates the perfect formula for token costs to continue increasing.
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And if maintaining these models is already expensive today, There will come a point when companies start asking themselves whether it makes sense to pay more for a simple tool.
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than what they used to pay many of the workers they laid off.
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Informazioni su questa lezione
In questa lezione, esploreremo come l'intelligenza artificiale (AI) ha influenzato l'occupazione e i costi aziendali nel mondo moderno. Attraverso l'analisi di un recente video, ci concentreremo su concetti chiave come l'efficienza dell'AI, il fenomeno del "token maxing" e l'impatto sui lavoratori. Questa pratica non solo migliorerà la tua comprensione dell'inglese, ma ti aiuterà anche a sviluppare abilità di shadowing che potrai applicare nei tuoi studi.
Vocabolario e frasi chiave
- Intelligenza artificiale (AI) - Vi fornisce la capacità di automatizzare e semplificare compiti complessi.
- Token maxing - L'uso eccessivo di token per dimostrare efficienza nel lavoro.
- Efficienza - La capacità di produrre il massimo risultato con il minimo sforzo.
- Costo - La spesa necessaria per l'utilizzo di tecnologie avanzate come l'AI.
- Dislocazione lavorativa - Il trasferimento di posti di lavoro da un settore all'altro a causa dell'automazione.
- Processi aziendali - Le routine e le operazioni seguite all'interno di un'azienda.
- Modelli AI - I diversi tipi di intelligenza artificiale utilizzati per vari scopi aziendali.
- Stato simbolico - Qualcosa che rappresenta uno status, in questo caso, il numero di token utilizzati.
Consigli di pratica
Per utilizzare al meglio questa lezione e migliorare le tue abilità di shadowspeak, segui questi suggerimenti pratici:
- Ascolto attivo: Fai attenzione al ritmo e al tono della voce nel video. Puoi ripetere le frasi subito dopo averle ascoltate per simulare il shadow speech. Questo metodo di shadowing in inglese ti aiuterà a migliorare la tua pronuncia e fluidità.
- Pratica in sezioni: Dividi il video in brevi segmenti. Concentrati su uno o due concetti alla volta e ripeti le informazioni fondamentali, permettendoti di assimilare meglio il contenuto.
- Riflettore: Quando utilizzi frasi complesse, rallenta e articolale chiaramente. Questo ti aiuterà non solo a parlare più chiaramente, ma anche a comprendere meglio le strutture linguistiche.
- Annotazioni: Prendi nota delle frasi chiave mentre ascolti. Rivedile per rafforzare la tua memoria e integrare nuovi vocaboli nel tuo parlato.
- Ripetizione coerente: Utilizza queste tecniche di shadowing regolarmente. La ripetizione è essenziale per consolidare le nuove abilità nel tuo parlato.
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | was released · be felt · was driven |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | has been · has started · have reported |
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.