Shadowing Practice: Every Generation Thinks This Time Is Different: The AI Job Myth - Learn English Speaking with Video
Bir pratik modu seçin
Her mod tam ekran bir pratik odası açar.
182 cümleler
Cümleler çok kısa veya çok uzun ise, ayarlamak için Düzenle'ye tıklayın.
1
If you're young and you're trying to figure out what to do with your career right now, you're probably hearing this.
2
AI is going to take your job.
3
And even if you aren't so young, you're probably hearing the same thing loudly from everyone.
4
And the honest response to that is every generation has heard some version of it about the machine of the moment.
5
Cars, electricity, the PC, the internet, smart phones, every single one of these was predicted to permanently destroy work.
6
Yep, and every single one created more total jobs than it destroyed.
7
Just different jobs.
8
Yes, so Jeff and I want to walk through the pattern because the pattern is real and it's consistent.
9
But from the inside the moment, it never looks like a pattern.
10
It always looks like the exception.
11
It always looks like this time is different.
12
Which is where I want to start, because this time has been said a lot of times.
13
All right, wave one, cars.
14
The turn of the 20th century, one of the biggest employers in most cities was the horse economy.
15
Oh, this is going to be my favorite segment.
16
You're right.
17
You're a barrel racer, so I can see why you would care about this.
18
I love me some fast horses, which is very inconvenient for a video about the technology that replaced them.
19
Sad.
20
But the good news is, nobody has built an AI that can turn a barrel yet.
21
And AI better never take the thrill of turning barrels away from me.
22
Sorry, go on.
23
The horse economy.
24
Don't worry, Bree.
25
No one wants to watch a robot rodeo.
26
So yeah, we've got stable hands, blacksmiths, carriage makers, farriers, grooms, feed suppliers, manure haulers.
27
Yeah, that was a real job title, believe it or not, and sometimes sounds a lot like my job at times.
28
New York City, around 1900, had roughly 200 ,000 working horses in it, producing millions of pounds of,
29
yeah, you guessed it, manure every day.
30
Which sounds like a job you'd want a machine to take.
31
Yeah, it turns out that you definitely want a machine to do that cars arrived, and within about 30 years, the horse economy had mostly gone.
32
Hundreds of thousands of jobs vanished.
33
And that's the version of the story that gets told.
34
The jobs that disappeared.
35
Right.
36
The part that doesn't get told, though, is about what happened next.
37
We had jobs that started to appear.
38
The auto industry created assembly line work, mechanics, gas station attendance, road construction, motels, drive -in restaurants, traffic engineers,
39
auto insurance, driver's ed teachers, an entire economy that just didn't exist before.
40
More jobs than the ones that were lost.
41
Way more and in fact none of them were predictable in 1900.
42
Nobody in 1899 said, you know what the future is?
43
Motel clerks.
44
Because nobody had thought of a motel yet.
45
Nobody had thought of a motel yet.
46
That's because they lacked the vision to see what was on the other side of the mountain.
47
They just saw the mountain.
48
Fast forward.
49
The same pattern shows up over and over.
50
Electricity kills the gaslighting industry.
51
I think you mean street lamp lighting industry because gas lighting is a different problem, right?
52
I mean the street lamp lighting industry and the ice cutting business more icebreakers That's right,
53
but it creates the appliance industry the entertainment industry
54
and eventually the entire computer Industry all of which were much bigger than what actually got replaced.
55
Yep The PC in the 1980s kills the typing tool and the filing clerk but creates software developers,
56
systems analysts, IT support, the entire tech sector.
57
The internet in the 90s kills travel agents, video rental stores, and encyclopedia salesmen.
58
Well, those encyclopedias were always a little bit cumbersome to carry around after all.
59
That's right.
60
But it creates e -commerce, digital marketing, UX designers, community managers,
61
and roughly every job that ends with the word engineer and did not exist in 1995.
62
Then smartphones after 2007 kill the standalone GPS unit and the MP3 player, but create the app economy,
63
the gig economy, and the creator economy.
64
It was millions of jobs.
65
Oh yeah.
66
And Jeff, here's the stat I want people to sit with.
67
David Otter at MIT has been doing this analysis for years.
68
His 2024 paper on new work found that more than 60 % of the jobs Americans do today didn't exist in 1940.
69
Hold on.
70
According to my calculations, that's more than half.
71
That's right.
72
More than half.
73
Those jobs weren't sitting there waiting to be discovered.
74
Humans invented them on top of the tools they were handed.
75
Again, these were all waiting just on the other side of the mountain.
76
What brings us to the next part, then, is what surprises people the most.
77
Bree, when ATMs first got installed everywhere in the 70s and 80s, what were bank tellers supposed to do?
78
Disappear, I guess?
79
Well, that's what a lot of people were predicting at that time.
80
You know, you've got a machine that dispenses cash, and that should be the end of tellers.
81
Well, what actually happened was the number of U .S bank tellers went up from around 300 ,000 in the mid -70s,
82
closer to 600 ,000 by 2010.
83
So, economist James Besson has the numbers on this one. Because?
84
Well, because the ATM that made a branch cheaper to run meant fewer tellers per branch, so banks opened more branches is.
85
And the job of the teller itself actually shifted from just counting cash to helping customers with more complicated stuff.
86
Same title, different work, and actually more of them.
87
Right.
88
This is a version of a very old economic principle, and this is your territory, Jeff.
89
Yeah, the very old stuff is always my territory, thanks.
90
Yeah, I'm referring specifically to a thing called Jevons paradox, which I've touched on in a previous video of mine.
91
It's wrote a book back in 1865, and no, I was not there when it was written, that was called The Coal Question.
92
His observation was simple.
93
When steam engines got more efficient, you didn't need as much coal to run one.
94
So the conventional wisdom held that coal consumption would go down as a result.
95
Plot twist, that's not what happened.
96
It went up.
97
You've seen this movie, haven't you, Bree?
98
Well, no spoilers for the folks at home.
99
Yes, it went way up.
100
And because the more efficient the machine, the engine, well, then the cheaper it got to operate and it could do more work for you.
101
And that meant more usage,
102
more people found more uses for these machines and therefore coal consumption actually increased.
103
That's right.
104
And the same pattern shows up with software.
105
That's right.
106
Same pattern.
107
AI makes writing software faster and cheaper, the naive prediction is great.
108
Now we need fewer developers.
109
But remember, what the Jevons pattern actually predicts is that more software gets written,
110
more things get built, that were too expensive or impractical to build before.
111
And demand for developers who can now do more actually goes up.
112
Which is not what the headlines usually say.
113
No, it turns out headlines don't do 160 -year -old economics
114
and headline writers don't often look over the other side of the mountain to see might be waiting there.
115
Okay, so here's the part I want people to hear.
116
Every generation has this exact conversation.
117
And it's with the exact same confidence.
118
Oh, yeah.
119
In 1930, John Maynard Keynes wrote an essay called Economic Possibilities for Our Grandchildren.
120
And in it, he coined the phrase technological unemployment.
121
His argument, machines are getting so productive that within 100 years,
122
we'd all be working 15 -hour weeks and running out of things to do.
123
Yeah, we were supposed to all have personal jetpacks by now, too, though.
124
But tell me, how's that working out for you, that 15 -hour work week?
125
Not so great, if you ask me.
126
I'm doing at least three of them every week.
127
Well, more is better, then.
128
So tell me about it.
129
On second thought, don't.
130
But you know what no company ever says?
131
I think we're finished.
132
We're done.
133
Nothing else left to do.
134
Nope, they don't say that.
135
Nature abhors a vacuum, and so do companies.
136
They will always fill the void with, wait for it, more work.
137
Right.
138
But we keep forgetting that.
139
In the 1960s, there was a presidential commission on automation because everyone was convinced the machines were about to make labor obsolete.
140
In the 1980s, it was Japanese robotics.
141
In the 2000s, it was offshoring.
142
Every wave had a very smart person saying, this time is different.
143
And each time the tools got adopted and humans reorganized around them.
144
Because the pattern is in tech eliminates jobs.
145
The pattern is tech eliminates tasks and humans invent new jobs around what's left.
146
I like your angle here Bree.
147
A job is a bundle of tasks.
148
So you've got some tasks in the bundle that get automated.
149
Well, the bundle regroups.
150
Sometimes new bundles form, sometimes people just get to do more interesting parts.
151
The Bay Teller got to spend less time counting 20s and start actually helping people.
152
The developer today gets to stop writing boilerplate code and start solving the actual problem.
153
And from inside the moment this always looks like the exception
154
because you can see the tasks getting eliminated in real time and you cannot see the jobs that haven't been invented yet.
155
That's because they're not on the other side of the mountain.
156
In 1918 -99 nobody saw the motel clerk over here.
157
And nobody in 2019 saw the prompt engineer.
158
Which is a six figure job now.
159
Which is a six figure job now.
160
That's exactly the point.
161
Somebody watching this video is going to have a job in 2040 with a title
162
that would sound completely made up if we said it out loud right now.
163
And they're going to be very good at it.
164
So if you're early in your career and the headlines are telling you your future is cooked, I'm going to let you in on a little secret.
165
You're actually not that special because we've done this before.
166
Right.
167
They're telling you what everybody's grandparents were told.
168
The tools change what the work looks like.
169
They don't change the fact that there's still more work to do than we have actual humans to do it.
170
Yep.
171
So learn the tools.
172
Get good at the parts of the work the tools can't do.
173
Get comfortable inventing the jobs that don't exist yet, because that's what every generation has actually done.
174
Even the ones that we're sure this time is going to be different.
175
Especially those.
176
In fact, despite what the prophets of doom have been saying,
177
the unemployment rate both in the US and globally has remained largely unchanged since the admin of generative AI.
178
So what that means is for every horror story of AI caused layoffs, many of
179
which were really just a way of hiding poor hiring practices
180
there must be a relatively equal number of hirings otherwise the rate would change
181
so jeff what's on the other side of this ai mountain well nobody knows for sure
182
but just remember this curious people don't get replaced they get liberated
Uygulamayı İndir
Akıcı konuşmak için ihtiyacınız olan her şey
AI TelaffuzHer cümleyi puanla
IPA AlıştırmasıHer sesi ustalaştır
Kelime BilgisiKelime bankanızı oluşturun
Kelime OyunuOynarken öğrenin
📺 Aynı Kanal
C12
What is Event Driven Architecture (EDA)?
Daily ConversationUniversity StudentsWorking Adults
C12
What Is Jev? The AI Model That Doesn't Generate Text
Daily ConversationUniversity StudentsWorking Adults
C18
AI, Machine Learning, Deep Learning and Generative AI Explained
Daily ConversationUniversity StudentsWorking Adults
C11
Spec-Driven Development: AI Assisted Coding Explained
Daily ConversationUniversity StudentsWorking Adults
C15
AI Agents vs Business Rules: Which Should Make Decisions?
Daily ConversationUniversity StudentsWorking Adults
C13
AI & Education: Generative AI & the Future of Critical Thinking
Business EnglishUniversity StudentsWorking Adults
C12
EDR vs. EPP vs. NGAV
Business EnglishWorking Adults
C13
MCP vs ADK: How Modern AI Agents Connect and Work Together
University StudentsWorking Adults
C11
Cybersecurity Architecture: Data Security
Business EnglishUniversity StudentsWorking Adults
C113
The 7 Skills You Need to Build AI Agents
Business EnglishUniversity StudentsWorking Adults
C118
How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact
Business EnglishUniversity StudentsWorking Adults
C111
What are AI Agents?
Business EnglishUniversity StudentsWorking Adults
C16
Cybersecurity Architecture: Five Principles to Follow (and One to Avoid)
Business EnglishUniversity StudentsWorking Adults
C11
Zero Trust Explained in 4 mins
Business EnglishUniversity StudentsWorking Adults
✨ Video Öner
B21
49 cümleler
AI & Future Jobs: Skills You NEED Now!
Daily ConversationWorking Adults
B21
460 cümleler
Why robots still can't pick strawberries
Daily ConversationUniversity StudentsWorking Adults
B223 cümleler
AI Made the Most Passive Side Hustle Even Easier
Daily ConversationWorking Adults
B21
339 cümleler
The (Overdue) Collapse of AI Layoffs (here's the proof)
Daily ConversationWorking Adults
B22
43 cümleler
Will robots take our jobs? | CNBC Explains
Daily ConversationWorking Adults
B213 cümleler
Why it's time to use AI
Daily ConversationWorking Adults
B21
154 cümleler
Are We Getting Too Comfortable With Claude?
Daily ConversationWorking Adults
B21
257 cümleler
Is AI About to Change Movies Forever?
Daily ConversationWorking Adults
B21
85 cümleler
AI Can Write Code. And That’s the Problem.
Daily ConversationUniversity StudentsWorking Adults
B21
546 cümleler
Do Elite Degrees Still Matter In The AI Era? | Jamie Beaton from Crimson Education | EP 410
Daily ConversationUniversity StudentsWorking Adults
B21
409 cümleler
I think AI slop is about to trigger a renaissance, actually
Daily ConversationUniversity StudentsWorking Adults
B22
20 cümleler
Is the AI Boom Just Another Bubble?
Daily ConversationWorking Adults
Bu dersin kelimeleri ve konuşma notları
Bu B2 seviyesindeki konuşma dersi “Every Generation Thinks This Time Is Different: The AI Job Myth” videosuna dayanıyor. Bu videoda gölgeleme çalışması için 182 cümle ve 1877 kelime var. Konuşma bölümü 12:33 sürüyor. Konuşmacı dakikada yaklaşık 150 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin %84’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.
Bu videodaki önemli kelimeler
Videodaki en ileri düzey 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| teller isim | /ˈtɛləɹ/ | anlatıcı, anlatan |
| motel isim | /moʊˈtɛl/ | motel |
| invent fiil | /ɪnˈvɛnt/ | icat etmek |
| bundle isim | /ˈbʌn.dl̩/ | bohça, demet |
| headline isim | /ˈhɛd.laɪn/ | başlık, manşet |
| developer isim | /dɪˈvɛləpɚ/ | geliştirici |
| clerk isim | /klɝk/ | yazman, kâtip |
| predict fiil | /pɹɪˈdɪkt/ | öngörmek, tahmin etmek |
| eliminate fiil | /ɪˈlɪm.ɪ.neɪt/ | temizlemek |
| barrel isim | /ˈbæɹəl/ | varil |
| manure isim | /məˈnʊɹ/ | gübre |
| encyclopedia isim | /ɪnˌsaɪkləˈpidi.ə/ | ansiklopedi |
| automobile isim | /ˈɔː.tə.məˌbil/ | otomobil, araba |
| lamp isim | /ˈlæ̝mp/ | lamba, ampul |
| disappear fiil | /dɪsəˈpɪɹ/ | gözden kaybolmak, yok olmak |
Duyacağınız deyimsel fiiller
| Kelime | Anlam |
|---|---|
| figure out fiil | açığa çıkarmak |
Tekrar etmeye değer cümleler
Videodan, günlük konuşmada yeniden kullanabileceğiniz kısa ve tam cümleler:
- According to my calculations, that's more than half.
- I'm doing at least three of them every week.
- They're telling you what everybody's grandparents were told.
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Present perfect continuous have/has been + -ing — önceden başlayıp hâlâ süren bir eylem | has been doing · have been saying |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | has heard · has been said · has built |
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | was predicted · has been said · were lost |
Dikkat edilecek telaffuzlar
Konuşmacı don't, you're, didn't gibi kısaltılmış biçimleri 42 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “th” sesleri: blacksmith /ˈblæk.smɪθ/, thrill /θɹɪl/
- “sh” ve “zh” sesleri: vanish /ˈvænɪʃ/, calculation /ˌkælkjuˈleɪʃn̩/, automation /ˌɔ.təˈmeɪ.ʃən/, offshore /ɒfˈʃɔː(ɹ)/, prediction /pɹɪˈdɪkʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: developer /dɪˈvɛləpɚ/, eliminate /ɪˈlɪm.ɪ.neɪt/, encyclopedia /ɪnˌsaɪkləˈpidi.ə/, automobile /ˈɔː.tə.məˌbil/, generative /ˈd͡ʒɛnəɹətɪv/
Türkçe konuşanların zorlandığı sesler:
- Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: clerk /klɝk/, predict /pɹɪˈdɪkt/, blacksmith /ˈblæk.smɪθ/, grandparent /ˈɡɹæn(d)pɛəɹənt/, spoiler /ˈspɔɪ.lɚ/
- /æ/ — “e”den daha açık: barrel /ˈbæɹəl/, lamp /ˈlæ̝mp/, blacksmith /ˈblæk.smɪθ/, grandparent /ˈɡɹæn(d)pɛəɹənt/, impractical /ɪmˈpɹæktɪkəl/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; teller, motel, invent gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.