Shadowing Practice: this is not financial advice... it's AI - Learn English Speaking with Video
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Back in the day, Wall Street was run by the people.
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You know the type: suits, nice watches, and egos the size of small countries.
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It was the Gordon Gekko and Jordan Belford archetypes.
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Because for most of modern financial history, investing with money managers and financial advisors was pretty straightforward.
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You hired a person, that person read through filings, listened to earnings calls, analyzed trading multiples, and then ignored all of it because he had a hunch about a biotech startup.
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It was a truly poetic system, because sometimes they were right.
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More often, they were wrong, but at least it felt human.
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And that's because there was always a story involved.
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Whether that story was the management looks strong, the balance sheet is stock feels cheap, which, if we're being honest,
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is just astrology for men who wear cufflinks.
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And for decades, crafting stories was the norm.
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Markets were inefficient, data moved slowly, and information advantages actually existed.
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It was the peak of capitalism and financial markets.
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But then, something happened that changed everything.
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The math nerd showed up, Guys named Yang, with PhDs from Harvard, three math Olympiad medals, and dreams that exclusively consist of probability distributions.
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And in the late 1980s, these nerds banded together because they realized something: markets weren't entirely random.
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They were statistical.
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Patterns exist, human behavior repeats, and market inefficiencies open the door for opportunities.
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And this is where quantitative investing began.
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Because instead of asking, "Is this a good company?" the quants were asking, "Given these variables, what's the expected outcome?" It was rules over narratives,
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probabilities over opinions, math over gut feelings.
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And it worked.
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Quantitative hedge funds began popping up out of nowhere like acne on a teenager, and the most infamous of which was Renaissance Technologies, and more specifically, the Medallion Fund.
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Renaissance Technologies' flagship hedge fund, and it was founded by mathematician Jim Simons.
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He believed in using purely mathematical and statistical models to predict market movements.
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Meaning no fundamental stock picking, no CNBC interviews, and no magic genie hiding inside a Bloomberg terminal.
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It was just purely math, algorithms, and machine learning.
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And it worked.
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Obscenely well.
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Because for more than two decades, the Medallion Fund generated over 60 % annualized returns before fees, and roughly 39 % after fees.
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And for context, Warren Buffett averaged around a 19 % annualized return over his career.
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you'd end up with around $324 ,000.
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But if you invested that same $10k with the Medallion Fund's returns, that $10k would have grown to a little over $7 million.
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So yeah, you could say the Medallion Fund was pretty successful.
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and it transformed the industry.
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Because instead of humans making all the judgment calls, now people leveraged systems that dynamically reacted to things like price movements,
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order flow, volatility, and countless other variables, and these algorithms operated at speeds that no human could ever compete with.
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And while Quant Funds were quietly becoming one of the favorite investment vehicles for institutions and high net worth individuals, there was something else unraveling with the everyday investors.
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do have the ability to outperform through discretionary stock picking.
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These people are very few and far between, and by the early 2000s, the data proving this was starting to pile up.
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Because it turns out that these active fund managers actually kinda stink at picking stocks.
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And not just occasionally, and not just during bad years, but consistently.
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And study after study showed that roughly 90 % of active equity fund managers underperform managing your money,
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it's even worse.
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So investors began asking questions like, "Why am I paying extra to underperform?" And this is where index investing enters the story.
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It's low -cost, passive, and designed to replicate the entire market's performance.
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And it was gaining popularity among the average everyday investors.
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Instead of searching for the needle in the haystack, investors simply began buying the whole haystack.
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So while retail investors were moving away from money managers and financial advisors, and towards index funds and ETFs, institutions were doing something similar.
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They were reallocating capital from traditional active fund managers toward other alternatives like quant firms, private markets, and in -house teams.
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Either way, we began seeing the traditional stock -picking money manager role slowly becoming obsolete.
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Capital was flowing out of the industry, fees were compressing, and careers evaporated.
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But even though this was a dying industry, there was still a demand for portfolio management and the benefits it includes.
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Things like rebalancing, asset allocation, and tax optimization.
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The boring stuff.
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And that's where we are today, and where robo -advisors come into the story.
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But before we go any further, let's clarify something first.
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If you hear that robo -advisors use algorithms to manage portfolios, you'd be right.
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But this is not in the same way that quantitative hedge funds use algorithms.
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Because quantitative hedge funds, like Renaissance Technologies, or Two Sigma, use algorithms to exploit micro -sigmas microsecond market inefficiencies,
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statistical arbitrages, and other active trading strategies, all in the pursuit of generating alpha.
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Comparing how quant firms use algorithms to how robo -advisors use algorithms is like comparing a Michelin star chef to Chef Boyardee,
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because robo -advisors are essentially just automated investment platforms designed to deliver low -cost market returns with minimal human involvement.
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They're not trying to compete with quant firms, they're involved with having a money manager.
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Robo -advisors handle the asset allocation, rebalancing, tax optimization, and the rest of the portfolio busywork.
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It's all the boring stuff your financial advisor would charge you 1 % a year to do in Excel.
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And investors love this value proposition.
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Platforms like Wealthfront, Betterment, and other robo -advisors began exploding in popularity.
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Because 10 years ago, Wealthfront had just $2 billion in assets under management.
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And if you fast forward to today, well, that number has grown 40 -fold to $80 billion in the world in total client assets.
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And it's not just Wealthfront.
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Globally, RoboAdvisors managed around $187 billion in 2017.
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And by 2023, that number had grown to nearly $2 .8 trillion.
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And by 2027, it's estimated to reach $4 .7 trillion.
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This isn't just a niche trend in finance.
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It's a structural shift in a long, stagnant industry.
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But it's not all sunshine and rainbows either, because robo -advisors have some blatant issues.
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But before I jump into that, a quick pause.
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Because this video is sponsored by Surfshark.
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Surfshark is a VPN service, and the reason I like it is pretty simple.
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It's built for people like me who don't want to constantly have to manage their privacy online.
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With Surfshark, you just turn it on, and your internet traffic is encrypted, your IP is hidden, and your data stays private.
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And the best part?
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You don't need to count how many devices you have, because with Surfshark, you can set up one account and use it on unlimited devices.
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It also comes with features like the Email Scam Checker, which helps detect phishing attempts and protect your personal information.
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And if your data does ever show up in a breach, Surfshark Alert notifies you right away.
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And that's why Surfshark is more than just your standard VPN.
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It's an all -in -one solution for online privacy.
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Because whether you're working from home or traveling abroad, Surfshark helps keep your information secure wherever you are.
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And they're so confident in their service, they offer a 30 -day money -back guarantee.
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If you want to check it out, you can go to surfshark .com /casualfinance, or just use code casualfinance at checkout, and get four extra months of Surfshark VPN.
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Thank you to Surfshark for sponsoring this video.
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And now back to robo -advisors and the issues that nobody seems to be talking about.
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Because on the surface, robo -advisors sell personal personalized investment solutions.
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But while they may feel like a sophisticated, tailor -made solution for your investment strategy, the truth is, they're nowhere near as personalized as they appear.
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Because they start by asking you to complete a questionnaire.
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It covers things like your age, income, time horizon, goals, and a few other questions.
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It feels intentional, thoughtful, and crafted specifically for you.
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But in reality, it's not.
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Because after you answer the questionnaire, instead of being crafted an individual investment plan for yourself,
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you're actually categorized into a bucket and slotted into a model portfolio that's shared by thousands of other people with similar answers.
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And that is where a lot of people misunderstand robo -advisors.
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They aren't some hyper -intelligent AI crafting a unique portfolio specifically for you.
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They were designed to replace the operational role of a traditional financial advisor.
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All the stuff that used to require meetings, paperwork, and a guy named Gary to explain beta with a laser pointer is no more.
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Because for decades, financial advisors weren't really paid for generating alpha or outperforming in the markets.
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They were paid for access.
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Access to markets, access to investment products, and access to information.
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And for a long time, that made sense.
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Markets were opaque, data was expensive, and investing without guidance felt like trying to assemble IKEA furniture without instructions.
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But then the internet showed up.
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Information asymmetry disappeared, market data became free, and research tools went mainstream.
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And suddenly financial advisors weren't guarding the gates anymore, and paying someone 1 % a year started to feel questionable.
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And that's the real draw of robo -advisors.
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And travel agents didn't disappear because people stopped traveling.
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They disappeared because booking a flight stopped requiring a phone call.
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Stockbrokers didn't disappear because people stopped trading.
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They disappeared because clicking a "Buy" or "Sell" button online was faster than calling some guy named "Rick" on the trading floor.
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technology.
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And that's exactly what's happening with robo -advisors.
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They're automating the middleman layer of the investment management world.
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Which is why this isn't a story about robo -advisors being good or bad.
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It's a story about what happens when technology finally reaches a stagnant industry.
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So now I'm curious to hear where you land on things.
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Have you ever used a robo -advisor?
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Would you trust one with your money?
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Drop it in the comments below and let's see how the investment landscape is changing.
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Because whether it's quant firms replacing discretionary managers or robo -advisors replacing traditional financial advisors, the direction is the same.
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When technology touches a stagnant industry, it doesn't make small tweaks.
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It forces fundamental changes.
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Vocabulary and speaking notes for this lesson
This B2 speaking lesson is built on the video “this is not financial advice... it's AI”. The speaker keeps coming back to these words: advisor, robo, fund, surfshark, financial. This video has 130 sentences and 1722 words to shadow. The speech runs for 10:28. The speaker talks at a natural 164 words per minute, close to everyday conversation. Only 74% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
The 15 most advanced words in the video, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| advisor noun | /ædˈvaɪ.zə(ɹ)/ | One who offers advice. |
| robo noun | /ˈɹoʊboʊ/ | A robot. |
| algorithm noun | /ˈælɡəɹɪðm̩/ | A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input… |
| investor noun | /ɪnˈvɛs.tə(ɹ)/ | A person who invests money in order to make a profit. |
| quantity noun | /ˈkwɑn(t)ɪti/ | A fundamental, generic term used when referring to the measurement (count, amount) of a scalar, vector, number of items or to some other way of denominating… |
| disappear verb | /dɪsəˈpɪɹ/ | To vanish. |
| quantitative adjective | /ˈkwɑn.təˌteɪ.tɪv/ | Of a measurements and data types: based on some quantity or number rather than on some quality. |
| hedge noun | /ˈhɛd͡ʒ/ | A thicket of bushes or other shrubbery, especially one planted as a fence between two portions of land, or to separate the parts of a garden. |
| portfolio noun | /pɔːtˈfəʊ.liˌəʊ/ | A case for carrying papers, drawings, photographs, maps and other flat documents. |
| mathematics noun | /mæθ(.ə)ˈmæt.ɪks/ | An abstract representational system studying numbers, shapes, structures, quantitative change and relationships between them. |
| stagnant adjective | /ˈstæɡnənt/ | Lacking flow or motion, and thus usually not fresh or healthy; decaying through stillness. |
| statistical adjective | /stəˈtɪs.tɪ.kəl/ | Of or pertaining to statistics. |
| annualized adjective | Arranged or written in an annual | |
| automate verb | /ˈɔ.təˌmeɪt/ | To replace or enhance human labor with machines. |
| discretionary adjective | /dɪsˈkɹɛʃəˌnɛɹi/ | Available at one's discretion; able to be used as one chooses; left to or regulated by one's own discretion or judgment. |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| show up verb | To appear, arrive, or attend, especially suddenly or erratically. | |
| pile up verb | To form a pile, stack, or heap. | |
| read through verb | To thoroughly read something to check or proofread it. | |
| set up verb | /ˌsɛt ˈʌp/ | To make ready for use. |
| turn out verb | To end up; to result. |
Pronunciation to watch
The speaker uses 23 contractions and reduced forms, such as they're, weren't, didn't. Say them the short way, as you hear them.
- The “th” sounds: algorithm /ˈælɡəɹɪðm̩/, mathematics /mæθ(.ə)ˈmæt.ɪks/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/, thoughtful /ˈθɔːtfəl/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
- The “sh” and “zh” sounds: discretionary /dɪsˈkɹɛʃəˌnɛɹi/, optimization /ˌɑptəmaɪˈzeɪʃən/, allocation /ˌæl.əˈkeɪ.ʃən/, arbitrage /ˈɑɹbɪˌtɹɑʒ/, inefficient /ˌɪn.ɪˈfɪʃ.ənt/
- Long words — get the stress right: quantitative /ˈkwɑn.təˌteɪ.tɪv/, portfolio /pɔːtˈfəʊ.liˌəʊ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, statistical /stəˈtɪs.tɪ.kəl/, discretionary /dɪsˈkɹɛʃəˌnɛɹi/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like advisor, robo, algorithm.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.