Prática de Shadowing: Alexandr Wang: “This is a Once-in-a-Civilization Opportunity” - Aprenda a falar inglês com vídeo
Escolha um modo de prática
Cada modo abre uma sala de prática em tela cheia.
416 frases
Se as frases estiverem muito curtas ou longas, clique em Edit para ajustá-las.
1
All right.
2
Full Rockstar treatment for Alexander Wang, everyone.
3
All right.
4
So, why don't we start out backstage.
5
We were saying, you know, one of the cool ways to think about this event is,
6
like, you know, this room is actually full of people who are just like us, But when we were 18 or 20,
7
you know, there's some 16 year olds in this audience, you know.
8
Let's jump to your story.
9
I mean, you came up always really smart, like math Olympiad.
10
Like, jump us to, you know, the Alex of that time.
11
Like, what were you feeling?
12
What were you thinking?
13
And what drove you down this road?
14
Yeah, well, I grew up in New Mexico, well, Salamis, New Mexico, which now Oppenheimer famous, but it really was the middle of nowhere.
15
And I remember I did all these math competitions, all these computer science competitions,
16
but then I knew I wanted to do really big things.
17
And it was like not exactly clear how or what the exact path to do that would be.
18
and I had a friend who was really into programming and after high school got an internship in the Valley.
19
I think his first internship was at Palantir.
20
And he was kind of this influence for me and so after I finished high school, I ended up working at Quora here in Silicon Valley.
21
And then I worked there for a year.
22
I took a gap year to work there and then I went to MIT.
23
I was 19 when I worked at Quora, I was 18 when I went to MIT, and I was 19 when I started Scale.
24
And I remember this period from like 17 to 19.
25
It was, I felt like I was constantly changing, like, you know, exactly what I wanted to do was constantly changing.
26
You know, I was learning so much just from the people around me, and it was just like,
27
I felt like I was drinking from the fire hose pretty constantly during that time.
28
and I would definitely recommend, you know, the two things that were really important.
29
One is I think working at a company was really valuable
30
because like I think from the outside in you have no idea how companies work.
31
You have no idea what it looks like to actually build something.
32
You have no idea what it looks like to iterate on something.
33
You have no idea what it looks like for groups of people to make decisions
34
and so I thought that was really important
35
and then going to school at MIT was actually really important
36
because it just gave me a lot of opportunity to explore what was interesting.
37
And so it was at MIT that I started training my first models and that I played around with TensorFlow,
38
which had just come out that year at MIT, and where I ultimately came up with the idea of scale.
39
And then after one year of MIT, I applied to YC.
40
It felt like a miracle to get in at that time.
41
And YC was really critical to my entrepreneurial journey.
42
Like, I don't think, like, YC is this amazing blend of, you know, they're very supportive and they obviously want you to succeed,
43
but they also give it to you very real and they tell you when you're being a dumbass, which I think is, you know,
44
that's what we all need in life.
45
So, yeah, that was, I think, the story till then I was 19, started Scale, and the rest is history.
46
I guess with, you worked with Jared Friedman at the time.
47
Yes.
48
And you came in with actually a a very different idea than what ended up becoming scale.
49
Yeah, so we wanted to build an AI agent, funnily enough, for Doc, to help people get medical care.
50
And it was a great example of an idea that I think will ultimately exist.
51
Like, I think we're even seeing it now.
52
Like, AI agents to help people get medical care are very real, but it was the wrong timing.
53
And we worked on it for about a month or two before Jared pulled us aside and we're like,
54
guys, this is, I don't know if this is going to go anywhere.
55
And that's exactly what we needed to hear.
56
And it was at that time when, like, you know, I had studied AI at MIT, I had, like, trained models,
57
and we thought, we sort of went back to the drawing board, thought deeply about where the opportunity was and came up with scale.
58
I guess selling data at the time, you know, large language models were not even, had not really come to the fore yet.
59
But self -driving cars were sort of coming up and computer vision suddenly became...
60
So that was sort of the first market, is that right?
61
Yeah, so the story here is that like I was...
62
When I was at MIT, I did a bunch of projects to train models of various forms.
63
And these were like, you know, by comparison today, they're like little toy models.
64
And I remember to train a model, I needed three things.
65
I needed a GCP account, like I need an account on some cloud service to get compute.
66
I needed the code to run, to actually train the model.
67
And I needed data.
68
I needed a data set.
69
And for two out of these three things, you could just press a button online and get them.
70
And then for the last one, for data, there was no effective way to get data for training these models.
71
And so it felt incredibly obvious that this was going to be the future, that there was going to be a way to,
72
you know, press a button, so to speak, and get data.
73
And it was very funny because in the years that followed, like in the first many years of scale, data was very unsexy still.
74
Every time we would go out to fundraise, even though our numbers were great and we had great revenue,
75
you know, VCs and investors would always be very skeptical.
76
They'd be like, oh, I don't know if this is a good business.
77
Does this have longevity?
78
Is this durable?
79
And it was really weird to me, but, you know, none of the investors had ever trained a model.
80
So I guess they didn't really get it.
81
And fast forward to today, you know, we managed to raise money.
82
We managed to keep going, managed to keep growing the business.
83
But the very same investors who passed on us
84
and were very dour on the potential of AI are writing think pieces today about how data is
85
so critical and is one of the biggest business opportunities in AI.
86
So it's very funny to see that whole thing come full circle.
87
It seems like that's actually a real good case study in first principles thinking, right?
88
Like you can't start a company by opening the pages of the Wall Street Journal and saying,
89
well, this data is hot like we're going to go work on that.
90
You literally couldn't have started scale that way.
91
You had to start from simple statements that are about the world that you know to be true,
92
and then sort of building something for that.
93
Yeah, I think the key thing is you need to develop conviction in a set of beliefs that nobody else agrees with.
94
I think if you look at all the most successful companies in the world,
95
they were started at a time long before the sort of like core idea was popular.
96
And they work on that.
97
They toil in obscurity for years and years before, you know, the idea or the space or the concept of the business, you know, becomes consensus.
98
And the only way you're going to be successful is if you're able to identify these truths about the world early, long before everyone else.
99
And I think that, like, I mean, one of the most surprising things, like, you know, scale, we've been working on AI for a decade,
100
you know, you just, you can't base your business decisions based on what everyone else is saying around you.
101
Like, if you go too much with the herd, you will get immensely confused, and you will end up nowhere.
102
And so you have to develop your own compass of what you think the future is going to look like,
103
because everyone else will just confuse you.
104
It seems like one of the things
105
you got incredibly great at was you start with this kernel of we believe X and nobody else believes it.
106
But then the mechanics of building the business are talking to investors and convincing them and not letting them demoralize you,
107
talking to customers who should just get it, and then especially convincing people to come work for you.
108
Yeah, I think that these early mechanics of building a company, these are things that I think you might have some predisposition to be good at,
109
but nobody is good at starting a company when they start a company.
110
And I remember talking to a lot of the investors who I met very early on, and a lot of them would say,
111
oh, you just grew so quickly and you changed so quickly, and I didn't see it at the time.
112
And I think that's probably true for literally everyone who starts a company.
113
Like, nobody is good at something they've never done before, right?
114
And so I think for all entrepreneurs, you start out pretty shitty at everything.
115
And the whole game is how do you develop yourself to continuously improve, to get better, and learn quickly?
116
Backstage, we were talking about this is actually a really lucky time to start a company because, you know, obviously you can come do YC.
117
You know, the people in this room have each other, which is kind of wild.
118
But not only that, now you have an ideal personal AI that's going to tell you, you know, hey, these are some ways to do it.
119
Do you think that would have helped you accelerate even faster?
120
What do you think it's like to start a company today with AI, in the age of AI?
121
Yeah.
122
Yeah.
123
I mean, I really think,
124
I think we're at this like amazing moment in the world where the bottleneck is not the progress of the AI models.
125
The bottleneck is diffusing that through the rest of the world.
126
And and helping the world adapt to this amazing technology that already exists.
127
Like, I think if the models didn't improve at all from today, there would still be, like, decades and decades of, like,
128
total upheaval and change in the economy and how the world operates and everything around us.
129
And so I think it's, as a result, it's, like, one of the most incredible,
130
it's probably a, like, once -in -a -civilization opportunity to be a dreamer
131
and to have a vision and to have ambition
132
and to impose a view of how the future world should look by building something amazing.
133
One of the things that we were chatting about backstage is when I started scale or 10 years ago,
134
if you start a company, you had to be you know, it was like David versus Goliath and you had to be clever
135
and you had to find like an angle into the market
136
and you had to figure out a way to compete even though you had much fewer resources.
137
And now I actually think with the power of agents and AI broadly speaking, it's much closer to Goliath versus Goliath.
138
But maybe the startup is like a mecha Goliath
139
that is vastly enhanced by the power of agents and AI and the large companies are the more traditional Goliath, so to speak.
140
But I think that startups now,
141
if you properly embrace AI agents and figure out the way to leverage their strengths in the most ambitious ways,
142
you can easily outcompete incumbents.
143
So let's talk about superintelligence, because clearly that's even in the name of your lab.
144
What does superintelligence mean operationally inside
145
Meta right now?
146
Yeah, I think that we, a year ago, Mark wrote this memo about personal superintelligence,
147
which I think actually is very similar to your concept of personal AGI.
148
But, you know, we believe that everybody in the world, you know, all the billions of people in the world are going to have a superintelligence
149
that is adapted and tailored to them, that enables them to accomplish their goals, knows their context, and ultimately is an expander of their own agency.
150
I think the thing that we think a lot about is agency expansion.
151
How do we help people accomplish things that they couldn't have ever dreamed of before?
152
And what would everyone in the world do if everything was just easy?
153
And we think about this in an ecosystem way as well.
154
I think Patrick mentioned it, but we don't believe in this totalizing, totalitarian view of AIs that control the world.
155
We believe that these are going to enhance this very broad ecosystem.
156
So, you know, we believe in billions of people all around the world all having their own personal super intelligence.
157
And we also believe in, you know, an explosion of entrepreneurship.
158
There's 200 million businesses that are on Metis platforms today.
159
We think that number should go to billions with this explosion of creativity and using AI tools.
160
And ultimately, we think that, you know, it's going to be this like dynamic ecosystem of business agents working with,
161
you know, personal agents and developing this sort of like, complex ecosystem that is fully AI supercharged.
162
So I was really psyched to see MetaSpark 1 .1.
163
My open claw absolutely loved it.
164
How has running a frontier lab been?
165
The MetaSpark level is sort of the opus level.
166
What's coming down the pipe?
167
And also I think that you're increasingly looking at open source, which I think this audience really loves.
168
Yeah, yeah.
169
Yeah.
170
So I think it was, it's been, you know, I've been at Meta for about a year now, and it's been quite a year.
171
I think, you know, getting in and, you know, Meta, we've talked about it publicly,
172
like long before, wasn't on the trajectory that was needed for Meta.
173
And so I got in there and we kind of did a zero -based build of how do you, you know, build an entire frontier lab,
174
you know, in some ways kind of from scratch, obviously using a lot of what we had.
175
and move as quickly as possible.
176
And so within nine months of that moment, we launched MuseSpark 1, and then two months later, we launched MuseImage and MuseSpark 1 .1.
177
And, you know, there's a few things that I think have really struck me about this.
178
You know, the first is talent density was incredibly important.
179
That was the core thing to bet on.
180
And, like, talent density is something that compounds naturally.
181
Like, the more talented people you have, the more of the most talented people want to join you.
182
You know, and I think it's kind of amazing to see on the inside, but, you know, frontier AI work is research.
183
Like, we are, it is scientific work.
184
We are exploring what can you do with these models?
185
How can you push these models?
186
What are the reaches of what can be accomplished with these models?
187
Which requires a totally different mindset and operating model than.. existed for internet companies or internet products and whatnot.
188
There's a lot more about experimentation, about science, about scaling.
189
And everything ultimately is about how do you develop a lab, an operating model, a system
190
that will just be able to compound with all of the exponential growth that will happen in the ecosystem.
191
Both the exponential growth in capabilities, the exponential growth in compute, the exponential growth in adoption and usage.
192
We are on this very steep exponent across maybe every dimension of the ecosystem.
193
It's important to develop an organism, that's how you think about the lab, that is able to grow with that.
194
It's been very exciting and we're going to be shipping a lot more.
195
I think we just launched MuseSpark 1 .1, which was a great model.
196
We're going to continue to have updates on the MuseSpark line.
197
We also have bigger models on the way
198
that I think will be much more competitive with even the very best models that are out there today.
199
We're going to be launching a harness soon
200
and have been working on a harness to help empower all the developers and agentic developers out there.
201
And then we're also, you know, as you mentioned, we're working on open source models.
202
And we want to kind of, as I described before, like we believe in a decentralized world of AI capability and progress and development.
203
We want to empower the broader ecosystem and everyone in the world to be able to build and develop using this technology.
204
And so we have a lot of exciting things on the way, and I think we want to be,
205
we want to empower the ecosystem and developers as much as humanly possible.
206
And it sounds like one of the ways, I mean, certainly when I was using MuseSpark with my OpenClaw,
207
like it became clear that it was as good as Opus, especially for that sort of agentic flow with skill files,
208
but it was like 8x cheaper, actually.
209
Yes.
210
Well, I think this goes to it.
211
We don't believe in a world where these models are
212
so expensive that they get rationed only for the most wealthy of developers and companies.
213
It's important for everyone to be able to use the technology and to build whatever they want to build with it.
214
And I think that, you know, we take a view, I think the best AI products haven't even been developed yet.
215
You know, if you look at the AI ecosystem and everything that's happened, like every wave is 10 times bigger than the past wave.
216
So, you know, when I started scale, the first wave was maybe self -driving cars.
217
Self -driving cars are really awesome.
218
They're like really, really cool.
219
But that was like pales in comparison to large language models and chatbots.
220
And like, you know, chatbots became this thing that was probably 10 times bigger even than self -driven cars.
221
And then there were coding agents, which came a few years later.
222
And coding agents were probably 10 times bigger than chatbots.
223
And I think we're just on this steep curve.
224
We're going to keep seeing these new modalities and form factors
225
and developments of the AI paradigm that will each be dramatically bigger than the last.
226
And so our point of view is, let's unleash the ecosystem, Let's explore and let's build the future of the world together.
227
What's the best way to actually take advantage of the coding model for MUSE Spark?
228
It's open code, right?
229
Yeah.
230
Today, the easiest way is to use open code.
231
We have onboarding on the website, and then soon we'll have a harness of our own.
232
Ultimately, I think we want great models
233
that plug into all of the available harnesses and empower as much combinatorial innovation in the ecosystem as possible.
234
Yeah, I know the harness is underwrapped still, but can you tease us with, I mean, I still use
235
Open Claw, I still use Hermes Agent.
236
These things are, I call them Ferraris that break down on the side of the road all the time.
237
Is this a Ferrari that won't break down?
238
Tease us a little bit.
239
Yeah, hopefully it doesn't break down.
240
I mean, I think we're really focused on speed.
241
I think speed is, for anyone that uses these tools, speed is probably one of the most critical things.
242
I think also reliability, like you mentioned, we want to be extremely reliable.
243
We want to be very extensible and to scale to as complex
244
and interesting of a multi -agent setup that you want to have.
245
Like, I think there's so much innovation that will occur even above the harness, frankly, in terms of, like,
246
how to orchestrate and set up loops and develop, like, you know, very complex ecosystems of these agents working together.
247
We want to be really extensible and ultimately we want to just empower people to harness this technology.
248
Because harness, actually, pun not intended.
249
But there's, like, I truly believe these models are already just incredibly powerful.
250
Like, they should be so powerful to fuel, you know, many, many points of expansion of GDP growth.
251
And I think it's like up to smart people with vision and ambition to make all that happen.
252
Let's see.
253
So one question.
254
I mean, when you look back on the decade,
255
what do you think they'll say was obvious in hindsight about AI that people are just missing in real time right now?
256
You know, so much of the debate that happens these days is around, oh, how good are the models actually getting?
257
And can the models actually bridge this issue?
258
And, you know, when are we going to get super intelligence?
259
Is that in like two years or five years
260
and you know are we going to hit a wall and you know so much of that debate is like
261
i think um in some ways uh a little bit of a waste of time
262
because you know i think it's inevitable
263
that we're going to have very powerful models
264
and um you know rather than i think we'll look back
265
and say oh all this arguing around like
266
when exactly was going to happen was sort of um was short -sighted
267
because the reality is we are just as a entire human civilization on this incredible exponential
268
but you cannot look at the progress of ai over the past decade
269
and not just be totally awestruck by how far
270
it's come like a decade ago the best ai models could recognize cats in youtube videos
271
and now you know we're talking to um you know a digital god
272
that can you know i mean we've seen some of the hacks and some of the things these systems are capable of.
273
And you just can't help but be awestruck.
274
And I think this trend will just continue.
275
Like these models are going to become more and more powerful.
276
And so I think a decade looking back, it'll be obvious that intelligence became abundant and that agency became abundant.
277
Like the current trends we're on are just going to keep continuing.
278
And this will be very strange.
279
I mean, I think for the history of humanity,
280
you know, groups of smart people getting together towards a shared goal was the bottleneck of progress.
281
You know, the United States of America, in some sense, was an example.
282
It's like the United States of America is formed from a group of very smart people getting together
283
and having a vision for the future that they wanted to enact.
284
And that's the story of nearly every company in America and the story of every YC company.
285
And that's going to change like all of a sudden the scarce resource isn't going to be intelligence
286
or agency I really think it's going to be vision
287
and ambition it's like do you have a clear view of
288
what you want the world to look like in the future what is the like one way in
289
which you want to put your finger on the scale for how the future of the world will develop
290
and how the how the world will look like in five to ten years
291
that it does not look like today and you have the ambition
292
and drive to like go through all the crap to make that happen.
293
And AI will make that easier.
294
Agents and AI makes that maybe 10 times or 100 times easier than it was a decade ago.
295
But the flip side of that is all of a sudden you can dream bigger.
296
And the world is like, there's so many things that need to evolve for us to be able to fully embrace this technology.
297
The world is really just barely even ready for this technology today.
298
And I think, you know, as a builder, we have a responsibility to prepare the world, right?
299
Like we have to help enterprises and governments, you know, to adapt to this new technology.
300
We have to help figure out how we secure the world from a biosecurity perspective or a cybersecurity perspective.
301
We have to figure out how we're going to manage all these risks that we see with this new technology.
302
But on the flip side, it's also the time of like, you know, unprecedented opportunity for humans.
303
Like we can develop new sciences.
304
We can solve problems in health and biology that have been forever unsolved.
305
We can build new businesses that you couldn't have even imagined before.
306
There's like new creative opportunities that couldn't have existed before.
307
So it's like it's just this incredible cradle of opportunity
308
and risks that I think makes it like no better time to be someone who's a builder
309
and has a strong view of how the world should change.
310
Do you think the path has changed?
311
I mean, one of the things I saw, I think Stanford, the amount of computer science majors actually dropped by some double -digit percentage.
312
People sort of worried, which is sort of insane to me.
313
You still sort of need those skills to even create agents that are that good.
314
Maybe that won't be true.
315
I'm not really sure.
316
What would you say to people in this audience right now?
317
This is sort of a real question
318
that people are sort of facing like should they become more word cell
319
and less shape rotator like what you know what's the move
320
and you know has that changed um the kind of people you're looking to hire
321
and you know how you manage your teams right now at meta i think systematic
322
and rigorous thinking are still incredibly important
323
because you know the abstraction layer i mean i didn't used to believe
324
that this is how this is going to play out but it really has like the abstraction layer just keeps changing.
325
So, you know, when I started a company, back in my day, we wrote code.
326
And now, you know, I'm sure nobody here writes code anymore.
327
That's ridiculous.
328
But now it's about how do you orchestrate the agents together?
329
And then it's like, how do you develop these organizations of agents?
330
Like, how do you get, like, a million agents to work together well?
331
And then it'll be, how do you get, like, a trillion agents to work together well?
332
I think
333
that there's going to be this continued need to figure out how you structure workflows at the abstraction layer
334
that we're going to be operating at.
335
And that form of rigorous, systematic thinking, I mean,
336
traditionally the way this would work in my era of starting companies is you would start by writing code,
337
and then you would have organizations of humans, and you'd figure out how to organize those humans.
338
And that requires us some thinking.
339
And now maybe it's much closer to first you orchestrate the agent, then you figure out how to orchestrate these armies of agents.
340
But I think systems thinking is never going to go out of style.
341
So I think it's definitely a mistake to go all in on word cell.
342
I think you need to shape rotate.
343
But then I think much more of what's necessary going into the future is having a deeper compass
344
and philosophical view on how the world should develop.
345
Because I think there are many, many lessons from human history around how we think civilization can go through this period.
346
And humanity will change more in the next decade than it has in the past 100 years, probably.
347
And so I think the imperative for us to have positive visions for
348
that and have coherent articulations of how that should develop are more important than ever.
349
Let's get a little more concrete.
350
I mean, one of the things I'm curious about is, like, are there sort of applications of AI
351
that you're seeing among your friends or internal to meta that you can talk about that are, you know, there's sort of obvious near -term,
352
maybe people haven't figured out yet.
353
I mean, give us some alpha.
354
I mean, I think there's still just like astronomical opportunity in agentic looping
355
and figuring out how you develop systems
356
that enable you to spend like 1000x more
357
or 1 millionx more on tokens to drive an outcome in a continuous feedback loop.
358
Like if you think about most companies, companies are just these like large scale feedback loops where humans are operating each of the edges.
359
It's like, you know, companies, they get customers and they figure out how to make those customers happier.
360
And if the customers are happier, then they spend more.
361
And if they spend more, they can hire more people who can then go figure out how to get more customers and make those customers happier.
362
And that's like this, you know, that in some sense is the feedback loop of every startup or every business.
363
And, you know, these within that, there are micro feedback loops that exist.
364
And I think developing agentic systems that can operate and optimize these feedback loops, there's just huge amounts of alpha there.
365
I think we've seen, internally in meta, cases where if you can develop the right agentic loop
366
and you have the right eval or the right metric for the agents to optimize,
367
you can have a swarm of agents accomplish more than a team of 100 engineers very,
368
very handily, actually very, very easily.
369
And so I think figuring out what the world looks like with lots of agentic coordination problems,
370
I think that is one of the most interesting problems today.
371
So mechanically speaking, Markdown files, cron jobs, and then basically pointing the agent at enough data
372
so that it can figure something out that maybe isn't in distribution.
373
Yeah, I think figuring out, yeah, yeah, mechanically figuring out what the metric is, and yeah, it just comes down to skills, markdown files, run jobs.
374
Slash goal.
375
Yeah, slash goal.
376
Like, I think it's always funny how mundane everything is once you really dig into it.
377
But it's not magic.
378
You know, I mean, some people put a lot of magic.
379
There's like some LinkedIn threads out there about some magic stuff.
380
Top advice.
381
Ignore LinkedIn.
382
Hey.
383
LinkedIn is where you get customers.
384
So I'd like to end on this, which is, you know, you get a telegram to send to the 18 -year -old version of yourself.
385
You know, what do you say to that person right now, given all, you know, I mean,
386
thank you for coming back and sharing your wisdom with this audience.
387
I mean, you know, what would you send in a message in a bottle to the 18 -year -old version of yourself right now?
388
Yeah, I think it really boils down to develop your own
389
internal compass for how you think the future will develop and have strong conviction in it because, you know.
390
you will get inundated with noise and people telling you shit.
391
And, like, you'll be very confusing and it'll be very hard.
392
And especially when you're young and you don't have experiences,
393
it can feel very difficult to have true conviction in what you believe and what you want to do.
394
But I think that's the most important thing.
395
Kind of as we talked about, you know, it took a deep, deep conviction in what we were building to be able to
396
weather the sort of storms of many years of chaos in the market, in the industry, in the people around us. And so,
397
and then the other piece of advice
398
I would have is try to identify what is the exponential in the world
399
that has both the steepest curve and will go the longest.
400
And, you know, many decades ago, this curve was Moore's Law.
401
And that probably was, you know, that was at the time, like clearly the right thing to invest on.
402
I think right now it's AI progress, but there will be more of these very steep curves in the future.
403
And it's fine if these curves start, you know, the starting point is very boring or like, it doesn't even seem that interesting.
404
Like, you know, when we started, when I started working on scale, you know, we had cat detectors in YouTube videos and that felt,
405
you know, it's hard to say, explain the story that that's like the most important technology of our time,
406
but it was on just this unbelievable exponential.
407
And I think I have one last thing I got to say.
408
Yes.
409
Yes.
410
Which is, we are, Meta is proud to offer everyone in this room $1 ,000 of free credits for the NewSpark API.
411
Fantastic.
412
And we're going to keep making the models better.
413
And right now NewSpark is, I think, 8x cheaper than Opus.
414
So if you convert that to Opus dollars, it's a lot more.
415
But no, everyone here will work to get everyone the details on how to get these credits, and we're really excited to see what everyone builds.
416
Alexander Wang, everyone.
Baixar aplicativo
Tudo o que você precisa para falar fluentemente
Pronúncia com IANota em cada frase
Prática de IPADomine cada som
VocabulárioMonte seu banco de palavras
Jogo de vocabulárioAprenda brincando
Mesmo canal
C11
Garry Tan: Own Your Intelligence
Daily ConversationWorking Adults
B22
Dot Plots: How to Actually See What Your Users Are Doing
Daily ConversationWorking Adults
B25
How Pitching Investors is Different Than Pitching Customers - Michael Seibel
Business EnglishWorking Adults
C115
Alexandr Wang on what matters in an age of abundant intelligence
Daily ConversationUniversity StudentsWorking Adults
B24
Jensen Huang: The Mindset That Built NVIDIA
Daily ConversationUniversity StudentsWorking Adults
B2How to Get Your First 10 Customers
Business EnglishWorking Adults
C1How to Build an AI-Native Services Company
Business EnglishUniversity StudentsWorking Adults
FOR YOU
Vídeo recomendado
C166
20 frases
Steve Jobs passion in work
Daily ConversationUniversity StudentsWorking Adults
C11
719 frases
View From The Top with Roelof Botha, Managing Partner of Sequoia Capital
Daily ConversationUniversity StudentsWorking Adults
C11
207 frases
Go’s Big Mistake
Daily ConversationUniversity StudentsWorking Adults
C11
274 frases
A 4-Step System to Process Information with Extreme Clarity
Daily ConversationUniversity StudentsWorking Adults
C11
346 frases
Trust the Process: How to Design Teams That Actually Work
Daily ConversationUniversity StudentsWorking Adults
C11
19 frases
Mark Zuckerberg: “Alex, are you sure this is going to work?”
Daily ConversationUniversity StudentsWorking Adults
C12
201 frases
Cleo Abram Answers The Web's Most Searched Questions | WIRED
Business EnglishDaily ConversationUniversity Students
C14
168 frases
The Rise Of Open-Source Software
Daily ConversationUniversity StudentsWorking Adults
C11
291 frases
How I Would Build a Personal Brand in 2027 (If I Had to Start Over)
Daily ConversationUniversity StudentsWorking Adults
C11
532 frases
How an Ex-Googler Accidentally Founded a Database Company
Daily ConversationUniversity StudentsWorking Adults
C17
372 frases
Why I Work So Hard Even Though I’ve Already "Made It"
Daily ConversationUniversity StudentsWorking Adults
Vocabulário e dicas de fala para esta lição
Esta aula de conversação usa o vídeo “Alexandr Wang: “This is a Once-in-a-Civilization Opportunity””. Este vídeo tem 416 frases e 5628 palavras para praticar shadowing. A fala dura 32:01. O falante fala em ritmo natural, cerca de 176 palavras por minuto, próximo de uma conversa do dia a dia. 85% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| ecosystem substantivo | /ˈikoʊˌsɪstəm/ | ecossistema |
| loop substantivo | /luːp/ | laço |
| harness verbo | /ˈhɑɹ.nəs/ | arrear, atrelar |
| incredibly advérbio | /ɪŋˈkɹɛdɪbli/ | incrivelmente |
| feedback substantivo | /ˈfiːdˌbæk/ | comentário, avaliação |
| curve substantivo | /kɚv/ | curva |
| investor substantivo | /ɪnˈvɛs.tə(ɹ)/ | investidor |
| empower verbo | /ɪmˈpaʊ.ə/ | dar poderes a, permitir |
| developer substantivo | /dɪˈvɛləpɚ/ | desenvolvedor, desenvolvedora |
| conviction substantivo | /kənˈvɪkʃən/ | convicção |
| accomplish verbo | /əˈkɑm.plɪʃ/ | conseguir |
| ambition substantivo | /æmˈbɪʃ.ən/ | ambição |
| layer substantivo | /ˈleɪ̯ɚ/ | camada |
| explore verbo | /ɪkˈsploɹ/ | explorar |
| civilization substantivo | /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/ | civilização |
Phrasal verbs que você vai ouvir
| Palavra | Pronúncia | Significado |
|---|---|---|
| figure out verbo | descobrir, deduzir | |
| end up verbo | terminar, acabar | |
| come up with verbo | pensar (em), alcançar | |
| get together verbo | reunir | |
| come back verbo | voltar | |
| come out verbo | /ˌkʌm ˈaʊt/ | sair, partir |
| set up verbo | /ˌsɛt ˈʌp/ | preparar |
Frases que vale a pena repetir
Frases curtas e completas do vídeo que você pode usar na conversa do dia a dia:
- Let's jump to your story.
- What were you thinking?
- You literally couldn't have started scale that way.
- How can you push these models?
- Do you think the path has changed?
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Present perfect continuous have/has been + -ing — uma ação que começou antes e continua | we've been working · have been working |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | they've never done · it's been · we've talked |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | were started · is adapted · was really psyched |
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | people who are · friend who was · everyone who starts |
Pronúncia para ficar de olho
O falante usa 75 contrações e formas reduzidas, como we're, don't, you're. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, thread /ˈθɹɛd/, wealthy /ˈwɛl.θi/
- Os sons de “sh” e “zh”: conviction /kənˈvɪkʃən/, accomplish /əˈkɑm.plɪʃ/, ambition /æmˈbɪʃ.ən/, civilization /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/, innovation /ˌɪn.əˈveɪ.ʃən/
- Palavras longas — acerte a sílaba tônica: ecosystem /ˈikoʊˌsɪstəm/, incredibly /ɪŋˈkɹɛdɪbli/, developer /dɪˈvɛləpɚ/, civilization /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/, humanity /hjuˈmænɪti/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: loop /luːp/, feedback /ˈfiːdˌbæk/, steep /stiːp/, adapt /əˈdæpt/, talented /ˈtæləntɪd/
- /l/ final — a língua toca o céu da boca, não vira “u”: enable /ɪˈneɪ.bəl/, ideal /aɪˈdi(ə)l/, skill /skɪl/, angle /ˈæŋ.ɡəl/, principle /ˈpɹɪn.sɪ.pəl/
- /h/ — um sopro suave, diferente do “r”: harness /ˈhɑɹ.nəs/, hire /ˈhaɪɹ/, nowhere /ˈnoʊ.(h)wɛɹ/, humanity /hjuˈmænɪti/, enhance /ɪnˈhæns/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como ecosystem, loop, harness.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.