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So as developers, we've been promised a lot.

Over the past two years, we've heard the same story said a thousand different ways.

With AI, you're going to become a 10x developer.

You're going to be able to get so much more done.

And now I got to be honest, on my side projects, oh, I feel it.

I'm seeing gains that blow past 10x.

But my side projects aren't exactly representative of the industry.

But even within the industry, everyone seems to be getting different results.

I have dev friends in just about every part of it, the big AI labs, the Silicon Valley startup that just raised $5 or $50 million,

as well as enterprise, banks, defense, healthcare, what have you.

And what I've noticed is just how different their experience with AI is.

Stripping out any of the red tape, when they're all allowed to use all the same models, all the same harnesses on the code bases,

some are seeing smaller gains, up to 2x, if that.

Whereas on the other hand, take a look at Lauren here.

She has shipped 2 ,000 good PRs in a real production code base in a single month.

So I wanted to find out why.

Not how she was able to ship all that

because that's a whole video on that tweet that you can go ahead and watch.

But why is there such a big gap in the benefits that devs are seeing from AI?

So I read through everything I could get my hands on.

And one of the best stories I found, they took 50 different teams in the same company working on

existing systems with a normal mix of software engineers from junior to senior and gave them all the same AI tools.

Well, except for a few.

And a bunch of those teams saw less than 3x improvements, whereas some other teams saw over 10x improvements.

And there are very clear reasons why that we can replicate and do ourselves.

But before we get into that, a quick word from today's sponsor.

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Now I created a fresh engineer bot here, connected it to my GitHub and I'm going to have it take a look at my Jetty repo,

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whether it be my phone, my laptop, or my desktop.

And after just a few minutes, there we have it.

The most urgent PRs in that repo are 81, 80, 85, and GitHub says they all merge cleanly.

So what I'm going to do is go ahead and merge the three most urgent PRs,

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Now, I know I already showed you this Lauren example, but she's not the only one shipping at these high rates.

You have Anthropic completely rebuilding their Git because Git couldn't handle their throughput of roughly 30 ,000 merges a day.

And these aren't even close to the only two examples.

They're just the most notable ones.

So if the labs are shipping like this, why can't normal companies?

Well, they kind of can.

And no, it is not by just throwing money at it.

I know that's the first thing everybody's going to say, because of course, it's going to cost money.

And these AI labs have infinite compute, and they're also heavily incentivized to ship with AI.

I get all that, but money is not the deciding factor.

What I mean is that if your company just started throwing money at a bunch of inference and tokens,

you're not going to be seeing these insane results.

There are a lot of other more crucial factors at play here, which is exactly what I went looking for.

I went through study after study, listen to stories from all these different companies reading all these different reports

and what i found was exactly the same as what i

was hearing from my friends the results are all over the place

i mean i saw anywhere from negative 19 increased output to you know 2x and above

but the story i mentioned earlier is what really caught my

eye to reiterate 50 ordinary teams in the same company working

on existing systems not building something from the ground up with a normal mix of junior

and senior software engineers with the results they saw being anywhere from under 3x

we don't know how low all the way to over 10x we don't know exactly how high

but that's the rough range from 3x to 10x and the company

that did that was amazon and i know okay they have money this

that and the other but there are countless there are countless companies

that have you know three developers on hand

and they're just throwing ai like crazy and they are shipping just as much as the examples I just gave you.

But that's never the example that people want to see.

They want to see it in an actual team environment that isn't just a startup, but a more established company.

And the only one that's giving these studies and these stories out to all of us, well, it's Amazon.

But they didn't just wake up one day and think, let's just throw AI at 50 teams.

It actually only started with one team.

So I want to lay the groundwork of how everything happened because it's important.

And they started it with a bedrock team, which makes a whole lot of sense considering,

well, that's the service that hosts Claude and GPT for AWS customers.

But they needed a whole new inference data plane.

To build this out, what they had estimated was they needed 30 people in 18 months to do it.

Instead, they took six engineers, gave them Kiro, which is their in -house codex or Claude alternative, and built it in two and a half months.

Which sounds insane, and it is, don't get me wrong, but it's a little bit less insane once you realize who did it.

The six engineers were some of the best in the entire company, people who already knew distributed systems and LLM architecture inside and out,

and two of which being distinguished software engineers.

So Amazon tried it again, but this time on Prime Video with a different six software engineers,

again using Kiro, and they gave them a 10 -day sprint.

And based on what they got done in that 10 -day sprint,

the project estimate that they were working on dropped from getting done in 90 weeks to only 24 weeks.

But something very important to note here, a senior engineer had spent three weeks before this even started writing out small,

detailed, well -scoped tasks for them to just be able to churn out.

And then we get to the actual 50 -team test.

Amazon Stores, which is Amazon .com and the physical stores, they ran this real test.

The 50 teams, the existing systems, the mix of junior to senior software engineers,

and just let them go wild using AI tools, Kiro to be exact.

And then they just kind of sat back and watched.

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All throughout 2025, they weren't looking at the amount of commits coming through.

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What they were looking at was how many things made it into production.

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And this is where we get the number where half of

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them actually came in under 3x the median was about 4 .5x, and with some teams being over 10x.

104

So how, with all of those variables in the same company, existing systems, mix of engineers, why is there such a discrepancy here?

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Well, it's actually pretty straightforward.

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So the teams that came in under 3x, they just took Kiro and just kind of sprinkled Kiro

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and some of the other tools that we have on top of their existing way of working.

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Any habits that they had in, you know, coming from previous sprints, they just, they didn't change anything other than plugging Kiro in.

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That's the wrong way to do things.

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I mean, they still saw results in that lower half, apparently up to three X, but the teams that did really well,

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fundamentally changed how they worked.

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And we know this because Amazon interviewed all of them

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and their takeaways were five habits and they call them habits instead of like hacks or tricks or whatever,

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because it, it truly does need to become a habit.

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You need to change the way you used to work into the way you're now going to work with AI moving forward.

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And the first habit of these teams that saw this success, they wrote everything down.

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Everything that was over in Slack, in their head, in the code review comments,

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and everywhere else, they took that and put it into their skills file, put it into their steering files,

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gave it to the AI as context one way or another for it to have access to.

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But not only that.

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Every single time the agent did something wrong, they didn't just, you know,

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type a bunch of cuss words at it because spoiler alert, AI isn't alive.

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You as the human need to take responsibility.

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They thought to themselves, what did I not tell it?

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Like, what did I do wrong for it to come up with this conclusion, which is not at all what I was asking?

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Like, how can I make this process better.

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And then as the models began to improve throughout 2025, they started removing rules because maybe I don't need to be

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so explicit with some things or give it these rules because the models are getting better.

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And that is an overall trend

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that we have seen over the past one to two years is that is you don't need as many rules.

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And by me saying that is also not a rule.

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It's just something I've personally experienced.

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Obviously people love, you know, the skills repose, the P -Stack, the Matt Pocock skills, all of those things.

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I'm not saying those are useless by any stretch of the imagination, especially not P -Stack.

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That's something that y 'all should absolutely go look into.

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But what I'm saying is that the more rules you have, the more context it has, you may just be blowing out the context.

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And what's nice about actually today than when this story took place is

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that you can connect your AI agent to all of these things.

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You can have it connected to C.

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all of the comments in the code review.

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You can have it connected to Slack, certain channels in Slack, so you can see all of those notes that you have written down.

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You don't necessarily have to pull it anymore from all of these different areas and give it,

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you know, put into the skills file or what have you,

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as long as you direct it and connect it to where it can get that information, as long as it's not in your head.

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And the second habit or thing that happened here is that all of the good teams got slower first.

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They noticed their productivity slipped at the very beginning.

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But again, that's because they are building out the process.

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They are building MCP servers for the AI agent so it can connect to everything that it needs to connect to.

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They're fixing error messages so the model could tell exactly what broke.

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They're even restructuring the entire code base so the AI agent could navigate it better, which I have thought about making an entire video on this

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of how to actually restructure your entire code base from pre -AI era to now with AI era.

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If that's of interest to you, just let me know.

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And some teams even went as far as changing languages.

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They went from maybe

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Python or JavaScript to obviously the superior TypeScript or Rust

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because you have the type checking

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and you have the compilers yelling at the agents for you

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so you don't have to do all of the back and forth.

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I have talked about this ad nauseum in my past videos, there is a little asterisk there that may,

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I will be going over in the next video or two, by the way.

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So stay tuned for that.

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I know two video plugs in a row.

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I'm sorry.

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The third habit was they didn't babysit the agents.

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Listen, if you're going back and forth with, you know, a single agent or a couple agents all day where you are,

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you know, responding to it every 30 seconds or minute, exactly when it is finished with a single task,

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you're never going to see the four or five, six X improvement because you're the bottleneck.

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You only have so much time in the day.

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What the frontier teams did, they actually only wrote one to 2 % of their code by hand.

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And for everything else, they would run agents that were looping for hours.

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And obviously they would run multiple at once working on their own things, which is much more common nowadays.

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You have to remember that this study, this deal happened in 2025.

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It was a whole lot less common at that point to be looping, having an agent loop for hours.

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For example, Ralph loop, if you remember that.

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I'm pretty sure that happened in 2026.

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It's been a long year.

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A lot has happened.

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So the way they got there is

179

that they were able to set it up and more or less ask the agent to keep going,

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check its own work, and then come back to me after everything has compiled, ran, all tests are passed,

181

which means that it's going and it's building and it's compiling and it's fixing those errors.

182

And then that'll have new errors and it's fixing those errors.

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And you get the idea.

184

This is actually kind of Lauren's whole thing too with P -Stack.

185

The idea there is to get like one agent that you, you build up to where you really trust it.

186

And only then do you start stacking them in parallel.

187

The fourth habit here is that they write the spec before the code.

188

Now this is more or less plan mode.

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I don't really use plan mode anymore.

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I use it for the first time the other day in a very long time, but the idea of it is still sound.

191

It's, you don't want to just give the AI a vague prompt.

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And then once the code comes back and however it's implemented,

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then you're arguing with it about the implementation and about the code and about changing it here,

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there and whatever, you want a clear picture to give to the AI of what you actually mean.

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This doesn't mean a whole entire spec list like this for every single thing you want.

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I actually think that's overkill and that actually makes it worse.

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You don't need to over -engineer your prompts, but you still need to be specific with your prompts,

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even if they are smaller now than they were before.

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This also derives from the fact that Amazon engineers, I think have always done spec -driven development, which is also built into their tool, Kiro.

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And also it saves money to go back

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and forth in regards to a document in plain English than trying to go through all of these diffs

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that the AI had already generated of all of this code and then trying to make the changes then.

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So not only is that the worst way to go about it, but it's also the more expensive way to go about it.

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And the fifth and final habit

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that they came to the conclusion of the 10x teams within Amazon is implementing something that everybody should already have implemented.

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Tests, linters, integration tests, performance tests, security tests, this whole entire pipeline,

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which is what I encouraged every single open source repository out there to build themselves

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if they want to continue taking external contributions because it just makes everything so much easier in the age of AI.

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Well, the material is built out properly, not only because you know you should do this

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but the better it is the higher the roi like the roi is there now more than ever before

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because that is one of the elements that lets the agent continue to loop

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and test beyond the compiler beyond the type checks things of

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that nature i guess that's included in there

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but to run all of these tests

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and run through the entire pipeline until it fixes is all of the mistakes that these tests in the pipeline find.

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And something interesting that they do is mocking out all of their services locally on their own laptop.

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So that way they can have a quick feedback loop with deterministic responses.

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Because it lets the agent do everything locally.

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Doing everything on your laptop without having to spin up a bunch of other services

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and connect to cloud services makes everything a lot faster.

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Because when you have a faster feedback loop, the more loops an agent can go through quickly

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and then the more loops that agent can go through in theory

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and in practice that we found the better the output so

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that that's the gap they all had the same tools it's just

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that you know the under 3x teams they didn't lay the groundwork

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they didn't write down the context they didn't have the pipeline in place

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and the tests in place

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and everything in place in order to remove themselves from the loop but what's craziest about all of this, which I mentioned the fact,

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but I really want to point it out here, is that all of this happened in 2025.

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Opus 4 .5 was released on November 24th of 2025, at the end of the year.

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All of this happened pre -Opus 4 .5.

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This is with models like Claude 3 .7, 4 .0, 4 .1, which if you use these models,

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you were keenly aware of just how bad they were relative to models today.

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I mean, you'd ask 3 .7 to make a small refactor somewhere in a single file,

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then it would come back with 20 changes that you didn't want from every corner of your code base.

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Imagine what the output of these teams looks like now with the models we have today.

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Oh, which reminds me, they're actually doing a new test in 2026.

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That's what 2026 is about for Amazon is how do we scale this out to more

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and more teams to the next 2000 teams instead of 50 teams.

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And oh boy, I can't wait to see the results on

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that one because if you have the right systems in place like the good teams at amazon do

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and you follow all

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that i laid out in this video models do greatly improve how much

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you can get done as well as the entire ecosystem being built around

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it i mean i hesitate to say this

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because i feel like it's just common sense

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but i know some people are on the outside of you know coding with ai

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and things of that nature so i'm going to say it anyway the amount of progress we have made from 2025,

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a year ago, when this story was taking place to today in terms of models

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and harnesses and other tools in the entire ecosystem and how they can all be connected.

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And also just how the models work.

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It is a night and day difference.

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So if some of these teams are seeing 10X back then, I can only imagine what they're getting done today.

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So if you're sitting around wondering, you know, where are all these 10X devs that we were promised, they're here.

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You're just not going to see it

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if you were solely relying on the model and giving in minimal effort into using it.

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It's the same idea, the same concept of working by yourself building something and working with a team building something.

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Those are two completely different systems and rules

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that you have to play by in order to optimize

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and be useful from how your code base is structured to how your tasks are structured to how everything ties in together.

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The other layer to that is AI agents.

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But that's all I got for you today.

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I hope you enjoyed the video.

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Hope you learned something.

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Hope you can take something away from this and apply it into your own development lifecycle.

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Or maybe you just want to sit here and watch more videos.

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If that's the case, well, there's a couple on screen that YouTube thinks you'll like.

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So give them a shot.

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Maybe you will.

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But anyway, I will see you in the next one.

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「I Looked Into How Devs Are Actually Using AI」を教材にしています。この動画には、シャドーイング用の文が270文、単語が3681語あります。音声の長さは18:34です。話す速さは速く、1分あたり約198語です。音のつながりや弱く発音される音が多くなります。単語の86%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
loop 名詞	/luːp/	ループ
connect 動詞	/kəˈnɛkt/	繋げる
habit 名詞	/ˈhæb.ɪt/	習慣
cloud 名詞	/ˈklaʊ̯d/	雲, 雲霞
pipeline 名詞	/ˈpaɪpˌlaɪn/	パイプライン
merge 動詞	/mɝd͡ʒ/	合わせる, 合併する
output 名詞	/ˈaʊtpʊt/	出力
insane 形容詞	/ɪnˈseɪn/	気が狂った, 狂しい
laptop 名詞	/ˈlæp.tɑp/	ノートパソコン, ラップトップ
implement 動詞	/ˈɪmpləmənt/	実行する, 実現する
sprint 名詞	/spɹɪnt/	スプリント, 短距離走
conclusion 名詞	/kənˈkluːʒən/	終わり, 結び
feedback 名詞	/ˈfiːdˌbæk/	フィードバック, 反応
urgent 形容詞	/ˈɜː.d͡ʒənt/	緊急の, 急を要する
plug 名詞	/plʌɡ/	プラグ

動画に出てくる句動詞

単語	発音	意味
go ahead 動詞	/ˌɡoʊ əˈhɛd/	どうぞお先に
go back 動詞		戻る, 帰る
write down 動詞		書き留める, 記す
come back 動詞		戻る, 帰る
find out 動詞		見出す, 調べる
try out 動詞		試す, 試してみる
wake up 動詞	/ˌweɪk ˈʌp/	目覚める, 目を覚ます

繰り返し練習したい文

動画に出てくる短く完結した文です。日常会話でそのまま使えます。

They thought to themselves, what did I not tell it?
It's just something I've personally experienced.
I don't really use plan mode anymore.

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	we've been promised · we've heard · I've noticed
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	we've been promised · be merged · being distinguished
関係詞節 who / which + 節 — 人や物について情報を加える	here, which is · team, which makes · Kiro, which is

注意したい発音

話し手はyou're, don't, they'reなど、短縮形や弱形を68回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: conclusion /kənˈkluːʒən/, contribution /ˌkɑntɹɪˈbjuʃən/, implementation /ˌɪmplɪmənˈteɪʃən/, crucial /ˈkɹuː.ʃəl/, imagination /ɪˌmæd͡ʒəˈneɪʃən/
長い単語（アクセントの位置に注意）: developer /dɪˈvɛləpɚ/, ecosystem /ˈikoʊˌsɪstəm/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, superior /sʊˈpɪɹ.i.ɚ/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: necessarily /ˌnɛs.əˈsɛɹ.ə.li/, relative /ˈɹɛl.ə.tɪv/, roughly /ˈɹʌf.li/, greatly /ˈɡɹeɪ̯t.li/, rely /ɹɪˈlaɪ/
/v/ — /b/ にならないように、上の歯を下唇に当てる: developer /dɪˈvɛləpɚ/, relative /ˈɹɛl.ə.tɪv/, server /ˈsɝvɚ/, productivity /ˌpɹɒdʌkˈtɪvəti/, variable [ˈvæɹ.i.ə.bl̩]
/f/ — 「フ」ではなく、上の歯と下唇で出す: fixing /ˈfɪksɪŋ/, feedback /ˈfiːdˌbæk/, helpful /ˈhɛlp.fl̩/, roughly /ˈɹʌf.li/, infinite /ˈɪnfɪnɪt/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。loop, connect, habitなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: I Looked Into How Devs Are Actually Using AI - 動画で英語スピーキングを学ぶ

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So I tried Matt's skills...