Ep 32 - Software 3.0: How LLMs Are Becoming the New Operating System

Send us a text

The digital world stands at a pivotal moment as we witness the emergence of Software 3.0 – a revolutionary paradigm where your words become code and language itself is the new programming interface. 

Throughout computing history, we've progressed through distinct software eras, each fundamentally changing who creates technology and how. Software 1.0 represented traditional hand-coding – powerful but brittle, requiring specialized knowledge and precision. Software 2.0 introduced machine learning, where data trained models to recognize patterns beyond explicit programming. Now, with Software 3.0, we simply communicate our intent to machines in natural language, and they respond with generated behavior.

What makes this shift truly revolutionary is how Large Language Models (LLMs) are evolving into a new kind of operating system. Rather than clicking through menus or writing code, we express our goals conversationally. The LLM interprets our intent, makes decisions, and coordinates tools on our behalf. As Andre Karpathy aptly notes, "Prompts have become the new source code, LLMs are the new runtime."

This transformation democratizes creation itself. You no longer need years of coding experience to build technology – if you can articulate an idea clearly, you can create. Karpathy himself built iOS apps without knowing Swift, using what he calls "vibe coding" – prototyping with feel and flow rather than formal specifications. We're witnessing creativity superseding traditional engineering approaches.

The most powerful metaphor for this new era might be Iron Man's suit – technology that doesn't replace Tony Stark but amplifies his capabilities. Similarly, these AI tools enhance human potential while keeping us firmly in control. The "autonomy slider" remains in our hands as we choose how much to delegate to our digital assistants.

We're still in the early days – the "1960s mainframe era" of LLMs – with enormous potential ahead. Whether you're a developer, founder, educator, or simply curious about the future, now is the time to engage with Software 3.0 and help shape where it goes next. Subscribe and join us as we continue exploring how AI is transforming our world and expanding human potential.

Want to join a community of AI learners and enthusiasts? AI Ready RVA is leading the conversation and is rapidly rising as a hub for AI in the Richmond Region. Become a member and support our AI literacy initiatives.

Episode Transcript

Speaker 1 (00:00):
Welcome back to Inspire AI, the podcast where we
explore how artificialintelligence is reshaping
business, creativity and the waywe live.
I'm your host, jason McGinty,and today we're diving into
something big, really bigSoftware.

(00:20):
The backbone of our world, isgoing through a seismic shift.
A new kind of computer hasemerged and its programming
language is English.
This isn't science fiction.
This is Software 3.0.
Let's start by zooming out.

(00:44):
If you look at the history ofcomputing, you can actually see
three distinct eras of softwaredevelopment.
Each has changed who gets tobuild software, how we build it
and what the computer actuallyis.
The first era is the hand-codedera.
We'll call it Software 1.0.
For most of the last 70 years,software was written by hand by
humans in programming languageslike c, java and python.

(01:10):
This is what andre carpathycalls software 1.0.
It's explicit, it's logical andit's painstakingly precise.
In software 1.0, you writeinstructions.
The machine follows them.
It's powerful, but it's alsobrittle.

(01:30):
If you forget a semicolon ormix up an index, the whole thing
breaks.
This is the era of IDEs, sourcecontrol, compiler errors and
stack overflow binges.
At midnight Then came machinelearning.
Instead of writing rules by hand, we started training models to

(01:52):
learn patterns from data.
This is software 2.0, codewritten by data, not by
developers.
You feed in examples, run anoptimizer and get back a model,
a neural network with millionsof parameters trained to make
decisions on its own.

(02:13):
This shift was huge.
It unlocked image recognition,recommendation systems, fraud
detection tasks that were nearlyimpossible to hand code.
But the catch you need massivedata, deep expertise, gpus,
tuning and lots and lots of math.

(02:35):
It's incredibly powerful, butit's not very accessible.
And now we've entered a thirdera, software 3.0.
Instead of writing code ortraining models, you talk to the
machine.
Yep, you prompt it.
You say write a function thatparses JSON, or generate a

(03:01):
product description in a casualtone, or plan my day based on
this calendar, and the machineresponds, not with a fixed reply
, but with generated behavior Innatural language and in your
own words.
This is why Carpathi says we'renow programming in English.

(03:24):
Prompts have become the newsource code, llms are the new
runtime.
It's not just a new tool, it'sa new paradigm.
And the big idea is this Ifprompts are programs and LLMs
are interpreting and executingthose programs, then the LLMs

(03:45):
are interpreting and executingthose programs.
Then the LLM itself is startingto look a lot like an operating
system, one that doesn't justrun apps.
It orchestrates reasoning,connects tools and adapts in
real time to your goals.
And we're not just rewritingsoftware, we're redefining what

(04:05):
software is.
Which brings us to that bigidea?
How exactly are LLMs becomingthe new OS?
Let's dig into that.
What does it really mean to saythat LLMs are becoming a new
kind of operating system?
It sounds bold, but it makesmore sense the more you think

(04:26):
about it.
In a traditional computer, theoperating system is what sits
between you and everything else.
It manages memory, runs yourapps and gives you an interface,
whether that's a terminal, adesktop or a touchscreen.
But today, more and more of ourinteractions with software are

(04:48):
going through a large languagemodel, which, of course, is a
massive shift.
Instead of clicking throughmenus or writing code, you just
talk to the computer.
You say things like Summarizethis document, refactor this
code file, find trends in thisspreadsheet and turn it into a

(05:09):
slide deck, and suddenly it'snot about commands or buttons,
it's about intent, and the LLMinterprets that intent, makes
decisions and executes the taskfor us.
So when Karpathy says LLMs arebecoming the new OS, he's not

(05:31):
just talking metaphorically.
He's talking about a new kindof interface layer, one that
understands language, makesdecisions and coordinates tools
on your behalf.
Think about it like this whenyou open a traditional operating
system like Windows or Mac OS,you're the one driving.

(05:54):
You launch the apps, move files, switch windows.
But with an LLM, it's like theOS meets you halfway.
You say what you want and itfigures out how to get there.
Sometimes it writes the code,sometimes it runs the steps,

(06:14):
Sometimes it even spins up toolsyou didn't know existed and,
just like an OS, it's become thehub, the platform.
Other apps are now being builton top of the LLM Apps like
Cursor for coding or Perplexityfor research.
They treat the LLM the wayolder apps treated the OS.

(06:38):
That's why this isn't just anupgrade.
It's a redefinition of whatsoftware even is.
We're not just using AI-poweredtools.
We're now living in anAI-powered environment.
And if that sounds wild, justwait.
We're still early, like 1960smainframe early.

(06:59):
Like 1960s mainframe early.
Llms are still mostly in thecloud.
They're still expensive,they're still limited by context
, windows, latency and hardware.
But we're moving fast and whatcomes next could look a lot like
a personal AI runtime, a futurewhere your AI knows you,

(07:23):
remembers your workflows andbecomes your default interface
for everything you do.
So, yes, we're entering the eraof software 3.0.
And the OS?
It talks, it listens and itthinks in tokens.
So, as Karpathy draws abrilliant analogy he says, llms

(07:48):
today have traits of utilities.
They're metered, cloud-basedservices.
You pay per million.
Tokens For those of you whohaven't been entrenched in AI
for very long, tokens For thoseof you who haven't been
entrenched in AI for very long.
What a token is is basicallyit's a unit of data processed by
AI models during its trainingand inference, which then

(08:12):
enables prediction or generationand reasoning.
You can think of a token like apart of a word or a short word
and since you pay in per milliontokens, they're treated like
electricity.
When open AI or Anthropic goesdown, the world feels the

(08:32):
blackout.
They also resemble fabs, akahigh capital factories, like
those that producesemiconductors used in
microchips.
Training a model like GPT-4 orClaude Opus requires enormous
investment, which only a fewplayers can do.

(08:53):
But perhaps most powerfully,llms are like operating systems.
They're ecosystems.
They manage memory, which arecontext windows, orchestrate
processes like function callingor agents, and interface with

(09:15):
both humans and machines.
They're not just a feature,they're a platform, and we're
still in the early days Like Isaid, 1960s of computing in the
early days.
Like I said, 1960s of computingcurrently, where cloud access
dominates and personal LLMs arerare but possible and the
desktop revolution for LLMshasn't happened Yet.

(09:39):
So what happens?
When we embed LLMs in our tools, you get partially autonomous
products.
Llms and our tools.
You get partially autonomousproducts like Cursor, which is
an AI code editor, or Perplexity, a research assistant that
cites sources and summarizesfindings.
These tools don't replacehumans.

(10:01):
They collaborate with them.
So how do they work?
They collaborate with them.
So how do they work?
First, context management,where the AI remembers and
structures your work.
Then there's multi-steporchestration the AI must
coordinate multiple models ortasks behind the scenes.
Then there's custom GUI, so youcan visually audit and approve

(10:28):
AI suggestions.
And finally there's theautonomy slider you choose how
much control you give the AIfrom autocomplete to run wild
and refactor my entire repo.
This design philosophy isn'tabout building agents that act
alone.
It's about building tools thatkeep humans in the loop Fast,

(10:51):
auditable, effective.
Think about it the Iron mansuit doesn't replace Tony Stark.
It amplifies him.
It extends his physicalcapabilities, enhances his
situational awareness.
It extends his physicalcapabilities, enhances his
situational awareness andautomates low-level tasks, but
he's still in control.
In the same way, llms are toolsthat enhance human capability,

(11:17):
not autonomous beings thatreplace us.
We're not building robots to doour jobs.
We're building suits to makeour work better, faster and
smarter.
One of the most beautifulconsequences of software 3.0,
now anyone can write software.

(11:38):
You don't need five years ofcoding experience to get started
.
If you can write a clearsentence in English, you can
build.
This is the essence of vibecoding Prototyping with feel and
flow, not formal specs.
Karpathy himself shared how hebuilt an iOS app without knowing

(12:03):
Swift, a programming languageused to build iOS apps, and then
another app, menugen, thatturned menu photos into images
of dishes, simply because hewanted it and could prompt it
into an existence.
We're moving from softwaredevelopment as engineering to
software development ascreativity, and this shift has

(12:25):
massive implications foreducation, entrepreneurship and
inclusion.
But there's a twist that mostpeople miss LLMs are not just
tools.
They are users of your softwaretoo.
We're entering a world wheredigital agents will read our

(12:45):
docs.
We're entering a world wheredigital agents will read our
docs, visit our websites andintegrate with our APIs, so we
have to build with them in mind.
This means creating LLMtxtfiles, which are a lot like

(13:06):
robotstxt, that help systemsunderstand your website.
It also means that offeringmarkdown-based documentation is
essential, replacing things likeclick here in your
documentation with curl commandsor structured JSON examples.
Building endpoints that agentscan use, not just people, that
agents can use, not just people.

(13:26):
If humans and agents willco-pilot the internet together,
we have to meet them halfway.
Let's zoom out as we wrap up.
Software is being rewritten, notjust in code, but in how we
think about computers,creativity and intelligence
itself.

(13:46):
Software 1.0 was written,software 2.0 was trained,
software 3.0 is prompted and,for the first time, the ability
to build doesn't belong to justengineers.
It belongs to anyone with anidea and a sentence.
We're entering the decade ofagents, not in the sense of hype

(14:10):
, but in the sense of slow,thoughtful, powerful
augmentation.
The Iron man suit is real.
The GUI for reasoning isemerging and the autonomy slider
is in your hand, so you choosehow much control to delegate to
the AI.
There is so much work to dohere, so many products to

(14:33):
rethink and so many dreams tobuild, and if you're listening
to this and thinking, this isthe moment I've been waiting for
.
You're not alone, whetheryou're a developer, founder,
educator or lifelong learner,this is your time to get fluent
in Software 3.0.

(14:54):
As always, thank you for tuningin to Inspire AI.
Subscribe, share and join usnext time as we continue
future-proofing with AI together.
Until then, keep prompting,keep building and keep your
hands on that autonomy slider.