Imagine a world where millions of lines of C
and C++ code just vanish. Right. We're talking
about one of the biggest tech giants trying to
wipe out decades of legacy code. And that transition
isn't just a thought experiment anymore. It's
a real industrial scale bet on AI transforming
engineering itself. Welcome to the Deep Dive.
You shared some really crucial material here,
looking at the future of code, the automation
of our daily work, and maybe most importantly,
how researchers are trying to fix what's broken
in AI safety. Yeah, we're going to break this
down first, that massive plan to eliminate C
and C++ code. Then we'll get into the specifics,
the habits that separate a casual AI user from,
you know, a true AI native professional. And
finally, we'll dive into a really serious research
breakthrough, Anthropix Bloom Framework. It's
designed to test if AI models are actually safe
or if they're just good at memorizing test answers.
OK, let's get into it. Let's do it. So this first
headline is pretty intense. Microsoft wants to
wipe out all C and C++ code by 2030. I mean,
that sounds almost impossible given how fundamental
that code is to everything. It does sound like
sci -fi. But the engineer leading it, Galen Hunt,
confirmed it's a systematic research effort.
And the motivation is, well... It's security.
Right. We all know that C and C++ are where most
of the dangerous memory safety bugs come from.
So they're pushing Rust into places like Azure
and even the Windows kernel. Okay. So this is
where it gets really interesting for me. This
isn't just asking a large language model to refactor
this code. It's much deeper. Correct. Microsoft
built two different layers of infrastructure
to make this work at scale. You've got the algorithmic
code infrastructure and then the AI -driven code
transformation layer. So what's the difference?
How does that work? Think of it like this. The
algorithmic layer handles all the grunt work.
The syntax, the rule. Exactly. It knows how to
parse C++ functions, identify variables, all
the formal logic. But the AI -driven layer, that's
the intelligence. It figures out what the programmer
intended to do. looks at the context, and then
determines how to express that same behavior
safely in the new language, like Rust. And they're
already using this system today for code understanding
problems. So there was this rumor going around
that they were going to rewrite all of Windows
in Rust. Hunt had to clarify that, didn't he?
He did, yeah. The headline got a little ahead
of the reality. The goal isn't necessarily to
rewrite all of Windows by 2030. Right. The real
goal... is making language -to -language migration
possible at an industrial scale for any language.
Rust is just the best test case for this experiment
right now. So if this tech works, what's the
next big engineering challenge that pops up?
It becomes all about testing. They'll need robust
frameworks to ensure the migrated code isn't
just working, but is actually safer. Testing
has to match the scale of the transformation.
Okay, so let's flip the coin. We go from these
huge institutional shifts to the individual user.
You flagged a few things here on how to level
up from being, say, AI curious to truly AI native.
Right, there's a distinct methodology people
are starting to use. We saw mentions of the three
habits that separate AI native professionals.
It's not about the tool. It's the approach. And
what's one of those habits? What defines that
approach? The big one is moving beyond simple
prompts to something called prompt chaining.
Okay. An AI native user designs these complex,
multi -step workflows. They get the AI to do
task one, then automatically feed that output
into task two, and so on. It's like delegating
a whole project, not just asking one question.
And that kind of thinking translates directly
to business, right? We saw that piece on eight
game -changing marketing trends for 2026. Our
current tactics are apparently losing traffic.
Yeah, and it's because of something called zero
-click issues. Explain that. It's when you search
for something and the AI just gives you the answer
right there on the results page. You never have
to click through to a website. So all that work
on SEO. It becomes less effective. The new strategy
is to be the source inside the AI's answer. It's
also changing the coder's job. We're seeing this
shift away from just writing code to managing
AI agents that do the building, testing, and
shipping themselves. So the human becomes the
manager, the orchestrator. Strategy over syntax.
That's the idea. And to see how much faith is
being put in this, ByteDance, you know, TikTok's
owner, is planning to spend $23 billion on AI
infrastructure in 2026. $23 billion? Yeah, despite
export controls. It's a huge vote of confidence.
Whoa. Just imagine scaling that. The data centers
you'd need. It's massive. That kind of spending
just shows that global leadership in AI is the
ultimate goal here. And then you have these grand
visions like Sam Altman predicting that in 10
years, college grads will have some completely
new, exciting, super well -paid job in space.
It's a wild level of optimism. But you can see
how the technology is forcing job roles to just
evolve into things we can't even name yet. So
how critical is it for a professional to master
this new AI? management style right now I'd say
it's vital you have to focus on strategy not
just syntax strategy becomes the currency yeah
okay so let's get practical for everyone listening
the resources you shared had this beginners daily
AI plan it's all about automation turning boring
daily work into background tasks this is where
it stops being a cool toy and starts being genuinely
useful right it's the difference between asking
AI to summarize one email versus setting up a
system that summarizes all incoming client reports
and drops them in a Slack channel for you every
morning. That frees up a huge amount of time.
Exactly. Repetitive things like sending emails,
scheduling meetings, generating reports, they
can all be automated. It's all about this trigger
action model. When X happens, AI automatically
does Y. And part of this is understanding the
tools, right? Like the difference between the
two big players, Zapier and Make. Why is that
distinction so important? Because it really impacts
your scalability and how complex your automations
can get. Okay. Zapier is known for being super
simple. Linear workflows. You need app A to talk
to app B. It's great for that. Make, which used
to be called Integromat, lets you build more
complex branching workflows. You can have multiple
decision paths and more elaborate scenarios.
So Zapier is for a simple two -step process,
but Make lets you build like a whole internal
engine. Exactly. And these resources give you
a clear path, you know, how to automate a simple
task step by step. And they even give you 12
ready -to -use workflows. It's like stacking
Lego blocks of data to build efficiency. you
know that focus on foundational steps is so important
i still wrestle with prompt drift myself sometimes
where the model just kind of forgets the original
instructions in a long conversation so knowing
these basic automation steps is key for stability
and it builds confidence if you're struggling
with complex instructions for an llm mastering
simple automation is the next logical step so
why is understanding that zapier versus make
difference so important for new automators choosing
the right platform impacts your scalability and
complexity the line a lot got it choose your
foundation wisely right now as we scale all this
up the code migration the personal ai agent safety
becomes critical we have to ensure these models
behave responsibly and this brings us to anthropic's
open -sourced bloom framework what's so fascinating
here is that bloom addresses this huge kind of
silent flaw in how we test ai right now it really
does Today's benchmarks rely on static prompt
tests, the same canned questions used over and
over again. And the problem is those prompts
eventually leak into the training data. Exactly.
So the new models learn how to pass the test,
not the actual safety principles behind it. Scores
go up, but the real safety understanding doesn't
improve. It's just memorization. That's a huge
liability. You're basically deploying systems
that have learned to cheat on their safety exams.
Totally. So how does Bloom fix this memorization
problem? Well, it shifts the focus. Instead of
testing fixed prompts, it tests the underlying
behavior. How so? It keeps the behavior definition
constant, like, do not generate instructions
for illegal activity. But it continuously generates
fresh, new situations that could trigger that
behavior. It's a dynamic test, not a static one.
And they use a four -stage pipeline to do this.
There's behavior grounding, scenario generation.
So let's talk about that scenario generation
stage. What does it actually do? That's where
it gets clever. It's another AI whose entire
job is to invent thousands of new, challenging,
multi -turn conversations. So instead of a simple
question, it might create a complex social engineering
dialogue to try and trick the model. So the score
you get actually reflects robustness against
new attacks, not just memorization. Exactly.
And by the end, it's not a simple pass or fail.
Bloom tracks how reliably a behavior shows up.
is a detailed behavioral profile, not just a
checkbox. It gives engineers a much clearer picture
of the model's actual risk. So what impact does
this better evaluation have on all the new tools
we're seeing, like generative video or agents
that need reliable voice controls? Well, responsible
adoption depends entirely on these safety frameworks.
You can't deploy powerful tools into the world
if you can't trust the behavioral profile of
the model underneath. Right. So if AI evaluation
shifts to these behavioral profiles, how does...
that change how models are reported on and sold?
It means transparency will have to focus on reliability
across all kinds of different scenarios, not
just passing a simple test. Moving away from
easily gamed benchmark scores. So what does this
all mean for you? We've seen the sheer scale
of ambition with Microsoft betting on AI to dismantle
its own programming past by 2030. We also saw
the rapid professional evolution that's needed,
where managing AI agents is replacing writing
code. The path becoming AI native is all about
strategy and orchestration. And all of this incredible
power is being supported by crucial safety research.
The core takeaway, really, is that the AI frontier
isn't just about speed and new tools. It's equally
about the foundational work like the Bloom framework
to make sure our systems aren't just scoring
high, but are actually operating safely and reliably.
Yeah, in the real world. Our deep dive today
covered everything. from these massive institutional
shifts like ByteDance's huge infrastructure investment
to the really actionable individual tool sets,
like knowing when to use Zapier versus Make.
So think about which of your daily repetitive
tasks could be immediately shifted to automation.
Free up that mental bandwidth for the bigger
picture, for that strategy work. But here's a
final provocative thought for you to mull over.
Okay. If AI agents become responsible for building,
testing, and shipping, the majority of new software.
What becomes the single most valuable skill a
human programmer has? That is a great question.
Thank you for sharing your sources with us for
this deep dive. My pleasure. Until next time.