쉐도잉 연습: Inside a YC Startup Disrupting a $900B Industry That Runs the US - 영상으로 영어 말하기 배우기
레슨 만드는 중...
1
enterprise companies don't want 90% accurate.
2
They want 99.999, as many nines as you can give them.
3
I barely interact with my software anymore.
4
If my agents can't use it, then we're gonna have to switch.
5
Our AI agents are digital identities that can do useful work across multiple channels.
6
Almost everything you own passed through hundreds of touch points between shippers, brokers, and carriers before it ever reached you.
7
Freight is an industry that quietly runs the world, And like most legacy industries, it still runs on phone calls, emails and spreadsheets.
8
But in this trip, I met a startup that's raised venture capital on the belief
9
that all of this is about to change.
10
AI allows us to take on more complex, flexible problems, but you can't just throw LMs blindly and expect them to do things.
11
You need to make sure that you're modeling the problem well.
12
Love the problem, but was looking for ways that I could be contributing more immediately to the industry, to the world, to society.
13
Today, I'm going behind the scenes with Vuma, a YC-backed startup building agentic architecture for freight and logistics,
14
handling everything from quoting a shipment to tracking the vehicle and collecting payment on the other side.
15
They've raised over $16 million with a Series A led by Kraft Ventures,
16
and their infrastructure is already inside some of the largest freight businesses in the US.
17
Trucking is a $900 billion industry projected to reach $1.46 trillion by 2035.
18
The future that we see is where you've got logistics professionals
19
that really understand the space that know all of the complexities
20
but have an army of AI workers that are operating on their behalf to help them do more.
21
A startup like this would not have been able to exist more than five years ago.
22
It just would have been impossible but it is the industry that literally runs the world.
23
So there's enough room for growth
24
but the real question I want to understand is how a startup this young actually wins in an industry this old.
25
One that's resisted automation for decades.
26
So today we're going behind the scenes with a company called Vuma.
27
They were originally YC-backed.
28
They've already raised Series A and they are specializing in freight and logistics, specifically software and AI tooling for companies around the world.
29
They're at a really interesting inflection point
30
and it's a good time for us to jump in
31
and see what's going on for them because the actual freight
32
and logistics industry is kind of predicted to have this huge surge in the next five years.
33
It's been in a bit of a lull as a whole
34
and great suppliers of software and AI tooling have been able to grow during those lulls
35
but now it's about to take another step up and so gearing up sales teams, gearing up product and engineering teams to actually account for this growth
36
and make sure that they can adequately capitalize on it is going to be a huge one for them.
37
Jesse, who is one of the founders, is an Aussie, which obviously is super cool.
38
Yeah, should be a bit of fun.
39
Let's go do it.
40
Dude, so I know we've got a lot of stuff going on today.
41
Yeah.
42
So, I mean, do you want to take us through kind of what's going on?
43
Yeah, so Monday mornings are a lot about like kicking off the week as a team.
44
So we have various products that we have.
45
So we'll have like individual product planning sessions and then we have a company all hands
46
where we all get together a little bit of celebrating the weekend
47
and then we learn on what the plan is for the week.
48
It's very like tactical.
49
Could you give us like a quick rundown on like what Vuma actually is?
50
Yeah, Vuma is a platform that allows logistics companies to build AI co-workers that do work from quote to cash.
51
So the agents will quote business, allow logistics companies to win freight, build that shipment in their system,
52
schedule appointments for the trucks, cover the trucks.
53
So like finding folks to actually move it and then tracking it end to end and getting paid on the back end.
54
So it's really a complete platform where our agents are communicating across email, voice and text to be able to get work done sort of tagline is to like win a move more freight.
55
What is that?
56
That looks familiar to me.
57
I know, this is like our tribute to Y Combinator, like just a good reminder to stay focused on what's important, making something people want.
58
There is a pretty high priority platform project for this month on sort of general data model updates and road mapping.
59
I think there are some immediate changes that we could make to the existing entities that we have, but I'd actually, if it's not urgent,
60
I'd like to sequence those after
61
having a chance to do some of the sort of longer-term work
62
because I still feel like we're maybe missing a bit of a concept around coverage strategy in general.
63
Okay, we might have just touched on this, but the Auto-Assign Carrier Contracts, which is from my main model, that does depend on the state and model change, right?
64
Not the whole thing, but the specific one.
65
And then there's also other prospects I think that needed?
66
No, it's primarily for VP Logistics.
67
Got it.
68
Okay, so those are two different...
69
But we have...
70
Yeah, we have time.
71
I think we should build this.
72
It will support many customers transitioning to us.
73
Yeah.
74
The goal is to try to get something built on that this month, but probably only in the last week of the month.
75
That's probably where it would fall, yeah.
76
Cool.
77
All right, big break.
78
Thank you.
79
Oh, there's no all hands in and bring rain.
80
No, no. Excellent.
81
So on Fridays we award the chef.
82
So the chef is whoever cooked the hardest last week.
83
So they get to wear the apron and the chef's hat. And
84
so then the chef from the prior week leads everyone in
85
a final clap at the end of the start of week meeting.
86
So you'll get to participate in that in a minute.
87
I'm looking forward to it.
88
We do these marketing videos
89
and we did you ever see the Volvo commercial where Jean-Claude Van Damme was standing between the two things.
90
So we did a remake of it.
91
It is AI generated, so it's me standing between two Boomer trucks, and then they do the splits.
92
And it was a bit of a.. it was great. But then...
93
It looks pretty good.
94
A lot of people...
95
I think a lot of people that don't understand how past AI is evolving actually thought it was me doing it.
96
I mean, it was super clearly AI.
97
Yeah.
98
But then I was like, oh, I should pretend like Jean-Claude Van Damme has beef with us
99
because I like stole his trick
100
and then I was like I went on a cameo
101
and he's on cameo
102
so then we just we got Jean-Claude Van Damme to send
103
a cameo being like Jesse you know like you're stealing my trick like
104
so it just came through
105
and we're gonna post it later this week it'll be fun
106
hi Jesse how are you this is Jean-Claude Van Damme apparently you tried to do my split it didn't work so well.
107
Mmm.
108
I heard you have a beautiful moustache, which is great.
109
And listen, do the split, try again, don't hurt yourself.
110
Make sure to warm up before, and if you don't do it, what's gonna happen to you?
111
Yeah.
112
Love you, man.
113
Talk to you soon, Jesse.
114
All right.
115
Should we get our weekly plans going?
116
Let's do it.
117
Can I brief you to the team just how we're thinking about this backlog user feedback?
118
There are, I think, 10 orgs that are currently on the track backlog waiting list, sort of waiting to go live.
119
We want to do, I don't know if user research is the right positioning,
120
but basically a check-in call with each of them to share the product progress over the last few months, what's live today, what's coming down the pike,
121
because the hypothesis, some of the users may be good to go live with what we have
122
and we can do that sooner and
123
or we can get feedback on what specifically is missing
124
and what they need to help inform like the next you know pieces of the roadmap.
125
The idea there is to try to like pull the learnings forward as much as we can
126
because something that we sometimes do is we've got products working
127
and then we like go full force into like get the deployments live as fast as possible.
128
What we want to do here is like bring all the
129
learnings as early as possible about like what are the gaps
130
so that we can do feature builds
131
while there are relatively fewer customers using it relatively less backward
132
compatibility to think about we'll be able to get those in place
133
and accelerate our our go-live after the fact as opposed to getting like you know three months in
134
and we find out that there's an important missing feature for some customer
135
and like having to sort of chase our tail there okay amazing who's chef last week chef
136
and well and for this This one needs to put the apron back on with the hat. For sure.
137
It's for your first sales call.
138
Yeah.
139
I signed it.
140
I signed it upside down the proper way.
141
Yeah, yeah.
142
That was for tradition.
143
Nice.
144
Alright, you on, Katis us down?
145
Uh...
146
3, 2, 1...
147
Okay, dude, so for those, again, who don't understand who you are, what you do within the company, would you mind running me through a bit of an intro?
148
So, Vuma, we have a pretty heavily technical problem we're building AI agents
149
that can do a really broad array of things in freight.
150
And it's not always true that you can kind of just take something off the shelf
151
and have it work super reliably at the levels of autonomy that we need our products to be able to operate.
152
Yeah.
153
So my background came from the world of self-driving vehicles, worked on self-driving cars and self-driving trucks.
154
I love the problem.
155
It was probably one of the best things that at that time
156
when I was sort of coming out of grad school you
157
could be be working on as an engineer who wanted to be you know working on very hard problems yeah
158
but it did have very long hardware timelines it's a fast moving industry right oh it absolutely is yeah
159
and just like so much room for automation though one of the reasons why I love the industry
160
so much is that it sits at this layer of the economy on top of
161
which like everything is built right
162
so it's a it's a necessary part of basically any physical products getting to its end user
163
From your perspective, what's the biggest bottleneck for you guys right now?
164
Yeah, I mean, a lot of it is about how quickly can we build into this space.
165
We started really narrow, focusing specifically on brokerage and specifically on the order entry product in brokerage.
166
And then over the past couple of years, we've expanded.
167
We do quoting, we do load coverage, scheduling, track, and now we're really seeing sort of all of Freight as like,
168
you know, the big sort of connected ecosystem that it is
169
and we think there are huge opportunities to like build across that space because that's actually the biggest challenge, how different the problems are that every business is solving.
170
The power that LLMs have to flexibly handle problems across domains like
171
that is a lot more powerful and unlocks things that were just never possible before.
172
It's really important for people to understand how complex this industry actually is
173
and how fragmented everything is and kind of would use the word chaotic in a way, right?
174
Definitely.
175
So having like agentic architecture to actually go and solve for that, you couldn't do that before, right?
176
It's a very good description.
177
Like it is chaotic and like every business sort of has their own way of doing things.
178
That's really why this industry has been historically so resistant to automation, because when you had to build rigid deterministic software,
179
the problems are solvable, but just the scale and the variety just didn't make it economical.
180
And now that equation's changed a little, and it hasn't gone away completely.
181
And there's a really interesting tension between I mean, how accurately do you try and model some canonical view of the industry
182
and the problems versus being flexible enough to adapt to what an individual customer has in terms of, you know, this is how we think about our loads, about scheduling, about coverage.
183
So that tension is what really makes it interesting.
184
Yeah, so number one, we're still going to do like basic algorithmic tests
185
because we feel like that's table fixed for this year.
186
But number two, we want to make this more realistic for how we write code of the movie, which is a lot of reviewing code,
187
which is a really great skill set to test, I think.
188
So we set a prompt to an AI agent, tells them the code, how do we know what's good, what's broken, architecture, is it sound, is this good to go, all that stuff.
189
Basically we want to create a fake repo
190
and have some PRs for it that are AI generated and broken in very specific ways.
191
What's the repo do you?
192
Yeah, so this was the other question, how big do we want to scope it?
193
We have two repos.
194
It's like one monorepo, but two folders within that.
195
One is a TypeScript project, one is a Python project.
196
Each one is like a tasks list, like a very basic feature.
197
One is a REST API with traditional MVC architecture, and one is GraphQL TypeScript.
198
So you can kind of choose whichever you're more familiar with.
199
Yeah.
200
Developers aren't going to be familiar with our exact stack, so it's not about testing those specific things, but just those different architectures.
201
So just to jump in for the audience's context here, this is your screening for potential new hires, is that right?
202
Yeah, exactly.
203
And we're kind of redesigning it because we're working with very AI forward engineers and the process is changing so quickly,
204
traditional evaluation techniques don't necessarily scale or don't necessarily measure what we're trying to measure anymore.
205
So trying to toe the line between traditional engineering skills required, but also people who will be really successful in the new world,
206
I think as you called it, weapons.
207
Yeah, exactly.
208
Like we, if all we did was check, like, can somebody write code?
209
And then they come in and they start working
210
and AI is writing 80% of the code like we're missing 80% of the signal basically
211
And there's a whole new there's a completely different set of skills to think about putting AI to work in
212
Sort of the most optimized or the most of leaned in way
213
And what we're trying to solve for is making sure
214
that our interview process is getting signal on how what candidates do that?
215
Yeah, it's super interesting how they're thinking about this in terms of hiring
216
because in this new AI enabled era The hiring process needs to be so much more thought out,
217
very intentional and also just very detailed in What they're actually requiring out of these potential candidates.
218
I think the other thing that is Probably most relevant here is that they have a lot of competitors.
219
There's bigger incumbents There's other startups at play here as we know We've seen them on the channel
220
and they need to be ruthless about the selection of people that they bring into this organization
221
if they want to be the best.
222
All of this actually works autonomously, but for the sake of the demo, we're kind of running through this like a bit manual.
223
Yeah, exactly.
224
So our tracking product will operate autonomously in the background
225
and you know there's various data feeds that are going to the agent that it's continuously evaluating.
226
So we're getting GPS pings from the vehicle, it's assessing is it going to be running late, is it fearing off course, has it stopped moving, is that expected,
227
is it the middle of the night and the agent may decide to do different things at various pieces.
228
And then typically when the load delivers, you want to confirm that that is the case
229
because sometimes you've got to start collecting documentation from the driver
230
because you can't pay them and you can't get paid until you have proof of that.
231
And so our agent will initiate and complete a lot of those workflows.
232
I'm going to hit create milestone here.
233
And what that's going to do is trigger an agent that is going to give us a phone call here.
234
So I'm actually getting a call right now.
235
Hey, Harold, this is Ashley with the Boomer tracking team.
236
I'm calling to confirm if you've unloaded at the delivery location.
237
Unload 1-0-0-0-3-2-8-0-6-6 in Atlantic City, New Jersey.
238
Let me know, are you fully unloaded?
239
Yeah, I got unloaded about an hour ago, so we're all set.
240
Got it, thanks.
241
I need a couple details.
242
What time did you arrive at the facility?
243
It was 2 p.m when I got here.
244
What time did you finish unloading and leave?
245
It was like 5 o'clock.
246
Thanks.
247
Just confirming that it's 5 p.m., right?
248
Yeah, that's right.
249
Appreciate it Harold, thanks for your time.
250
Take care.
251
Alright, see you later Ashley, thank you.
252
Ashley?
253
Ashley, that's what we call her.
254
What advice would you give to that person who's earlier in their journey, who's really trying to figure this out and get some footing in this space?
255
One is like don't listen to too much advice because I feel like if you're gonna be a founder, you've just gotta figure shit out for yourself.
256
I definitely, early in my journey, relied on the crutch of wanting people to like have the answers for me,
257
but really part of the journey is how quickly can you figure things out
258
and respond to feedback that you're getting from the market.
259
But the other thing I think is think really big and be prepared to shift and change your ideas really quickly,
260
early on, until you hit on the thing
261
that feels like there's something here worth committing a big part of your life to.
262
And so some of the best teams, and I think the thing that we've tried to channel is that.
263
It's like constantly moving to find the thing that is the most powerful and impactful.
264
And then trying to be the best in the world at it.
📺 같은 채널
✨ 추천 영상
B22
60 문장
We're trying something new...
Business EnglishUniversity StudentsWorking Adults
B22
43 문장
Business Partner B2 Unit 4.1 Alternative video
Business EnglishUniversity StudentsWorking Adults
B21
323 문장
How Jeff Bezos Actually Built Amazon
Business EnglishUniversity StudentsWorking Adults
B21
486 문장
From $0 To $2M Trading Prop Firms In 20 Months
Business EnglishUniversity StudentsWorking Adults
B21
169 문장
World’s most advanced robotic warehouse (AI automation)
Business EnglishUniversity StudentsWorking Adults
B21
277 문장
The New Canadian EV Car Could End the Entire EV Industry!
Business EnglishUniversity StudentsWorking Adults
B21
507 문장
Exposing a $300,000,000 Scam
Business EnglishUniversity StudentsWorking Adults
B21
142 문장
How Americans see EU Tech
Business EnglishUniversity StudentsWorking Adults
B21
190 문장
This 25-Year-Old Built a 2-Year Moat Nobody Can Bet Against | Corgi, Nico Laqua & Emily Yuan
Business EnglishUniversity StudentsWorking Adults
B280 문장
How to Build An Investor Pitch Deck For Y Combinator
Business EnglishInterview EnglishUniversity Students
B2171 문장
I built an app and no one's using it
Business EnglishUniversity StudentsWorking Adults
이 레슨에서 연습할 내용
이 비디오는 미국의 물류 산업을 혁신하려는 YC 스타트업 Vuma에 대한 이야기입니다. 물류, AI, 스타트업 등의 주제를 다루며, 전문 용어와 일상 대화가 섞여 있습니다. 영어 쉐도잉을 통해 빠른 속도의 대화에 익숙해지고, 전문 용어의 발음과 의미를 학습할 수 있습니다. 특히 "shadowspeaks"나 "shadow speak"와 같은 쉐도잉 기술을 적용하여 발음 교정을 연습해 보세요.
핵심 어휘와 구문
- Freight and logistics: 물류와 운송. "Freight"는 화물을, "logistics"는 물류 체계를 의미합니다.
- AI co-workers: AI 동료. 인공지능이 사람과 함께 일하는 역할을 말합니다.
- Quote to cash: 견적부터 결제까지. 비즈니스 프로세스의 전 과정을 나타냅니다.
- Legacy industry: 전통 산업. 오래된 방식을 유지하는 산업을 말합니다.
- Series A: A 라운드 투자. 스타트업이 받는 두 번째 큰 투자 단계입니다.
쉐도잉 연습 팁
이 비디오의 대화는 자연스러운 속도로 진행되며, 전문 용어가 많아 집중력이 필요합니다. 영어 쉐도잉을 할 때는 다음과 같이 연습하세요. 먼저 비디오를 2-3번 듣고, 문장의 리듬과 강세를 파악합니다. 특히 "AI agents are digital identities that can do useful work across multiple channels"와 같은 긴 문장은 구절로 나누어 연습하세요. "shadowspeak" 기술을 사용하여 발음을 정확히 모방하고, 영어 발음 교정에 집중하세요. 또한, 주인공들의 대화에서 느낀 감정을 표현하는 톤도 따라해 보면 자연스러운 영어 표현이 가능해집니다. 작은 구절부터 시작하여 점차 길게 늘려가면 효과적입니다.
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | They've raised · They've already raised · have been |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | Contracts, which is · moustache, which is · movie, which is |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | is built · be prepared |
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.