The AI Daily Brief: Artificial Intelligence News and Analysis - AI, Agents and Software 3.0
Episode Date: June 29, 2025Andrej Karpathy's Software 3.0 talk reframes LLMs as a new kind of software—programmable, agent-native, and fundamentally different from past computing models. This episode breaks down his key i...deas, from autonomy sliders to the need for new infrastructure designed for AI-first users.Source: https://www.youtube.com/watch?v=LCEmiRjPEtQGet Ad Free AI Daily Brief: https://patreon.com/AIDailyBriefBrought to you by:Gemini - Supercharge your creativity and productivity - http://gemini.google/KPMG – Go to https://kpmg.com/ai to learn more about how KPMG can help you drive value with our AI solutions.Blitzy.com - Go to https://blitzy.com/ to build enterprise software in days, not months AGNTCY - The AGNTCY is an open-source collective dedicated to building the Internet of Agents, enabling AI agents to communicate and collaborate seamlessly across frameworks. Join a community of engineers focused on high-quality multi-agent software and support the initiative at agntcy.org Vanta - Simplify compliance - https://vanta.com/nlwPlumb - The automation platform for AI experts and consultants https://useplumb.com/The Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdownInterested in sponsoring the show? nlw@breakdown.network
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Today on the AI Daily Brief, software in the era of AI or software 3.0.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
Hello, friends, quick announcements.
First of all, thank you to our sponsors for today's show, KPMG, Blitzy, Plum, and Vanta.
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Now, today is one of our weekend episodes,
classically are Long Reads episodes, which really in some ways are,
aren't about long reads as much as big ideas. And we have a very big idea to explore today.
This one comes courtesy of former OpenAI co-founder Andre Carpathy. A couple of weeks ago,
Andre gave the keynote at the YC Startup School. Subsequent to that speech, it was published to
YouTube. You should watch the whole thing. I will include a link. We're going to discuss it and
try to contextualize it, so this is not meant to just repeat what Andre said. But the funny thing
about it is that the video wasn't posted right away. And people were going so crazy for the
and there were so many tweets and X posts about it that the fine folks at latent space
actually were able to hack together the slide deck out of clips and pictures from X.
The speech is in many ways about redesigning the world of software for LLM Native operations
and LLMs being a new type of computing.
And one of the really interesting things that Andre notes is that while software stayed largely
the same, at least from a paradigm perspective for about 70 years, we have now had two big shifts
in a very short period of time.
And we'll get into it a moment his articulation of those shifts,
but you can also see this just in the discourse around the engineering field.
A couple of years ago, latent space wrote an incredibly important post called the rise of the
AI engineer.
And the distinction that Swix was trying to draw here was that when we were talking about
AI engineers now, we were no longer just talking about machine learning researchers and data
scientists.
We weren't just talking about people who were dealing with training and evaluation and inference
and data.
we were dealing with people who were building on top of this new ecosystem focused on product
and taking advantage of foundation models, agents, new tooling, and infrastructure to redesign
how people interact with software. That piece actually quoted Andre Carpathy back then as well,
when he said, in numbers, there's probably going to be significantly more AI engineers than there
are ML engineers. And at the time, Swix was trying to put some context around what this actually meant.
He said that there are no end of challenges in successfully evaluating, applying, and productizing
AI. He talked about model selection, tool selection, and just keeping on top of research,
progress, and new opportunities. The conclusion, which seems so obvious now, is that this
was a full-time job. Quote, I think software engineering will spawn a new sub-discipline, specializing
in applications of AI, and wielding the emerging stack effectively, just as site reliability
engineer, DevOps engineer, data engineer, and analytics engineer emerged. The emerging and least
cringe version of this role seems to be AI engineer. Now, even what AI engineering means has continued
to evolve over the last couple of years. If you were listening earlier this week, we talked all about
context engineering. The definition that Langchains Harrison Chase gives is this. Context engineering
is building dynamic systems to provide the right information and tools in the right format,
such that the LLM can plausibly accomplish the task. In other words, it's about giving AI models the
context they need to accomplish their goals, something that's become even more important in the architecture
of agents, which are dealing with much more context and much more complexity. The point is that the
very field of software and engineering is continuing to evolve, and that's basically the context
for Andre's speech. In software 1.0, it was computer code being written by humans to program a
computer. Software 2.0, which Andre wrote about a number of years ago, shifted computer code to
neural network weights learned from data, with the output being the neural net itself. In software 3.0,
large language models can themselves be programmed with natural language prompts.
To quote Andre himself from back in January 2023,
the hottest new programming language is English.
Discussing the transition from software 1 to software 2,
Carpathy drew on his time at Tesla.
As the company built out autopilot, the code base was largely written in C+++,
but most of the visual data was handled by the neural network.
Over time, as the autopilot improved, the neural network component grew
while C++ code was deleted.
Carpathy said the software 2.0 stack quite literally 8 through 3.3,000,
the software stack of the autopilot. He believes we're seeing the same thing again with the proliferation
of LLMs. Carpathy described LLMs as functionally a type of programmable neural network. Rather than a
set path, the user can program the LLMs to produce a variety of different outcomes. Now, this is not
about vibe coding or getting an LLM to spit out lines of traditional code. This is about shifting our
thinking to consider the use of LLMs themselves as an entirely new type of software. By way of example,
if you're prompting an LLM to produce a deep research report, that's akin to writing a Python script
that could search the web and summarize data. Of course, there are a huge number of differences,
but the key point is that we're talking about using an LLM to achieve a particular outcome
in the same way you would use a traditional program. And because of all this, he argued that we
need to think about LLMs in a slightly different way. He argued effectively that AI is the new electricity,
and pointed out that LLMs feel like they have the properties of utilities right now. Carpathy drew links
to how infrastructure is built, how tokens are metered, and even how brownouts in AI when a major
service goes down, can be similar to the electricity shutting off. He also argued that LLMs are like
computer chip fabs, that they require massive CAPEX and have deeply held secrets in their construction,
naturally trending towards a small number of powerful players. Finally, though, he settled on the
analogy of LLMs as operating systems. Rather than thinking of LLMs as similar to electricity,
where every electron is the same, he argued that LLMs are now complex ecosystems, where there's
differentiated functionality, tool use, and performance. Giving a direct example, he noted that
cursor can be run using models from OpenAI, Google, or Anthropic, each with different outcomes.
Looking towards the future, he noted that we're still in the 1970s era for the LLM computer,
with large centralized players serving a very finite amount of compute.
But Carpathy anticipates something similar to the PC Revolution coming to LLMs,
with users able to run them on their own hardware eventually.
Taking the analogy further, he suggested that current LLMs are still very similar to using
an operating system directly through the terminal, arguing, I think a GUI hasn't yet been
invested in a general way. Shouldn't chatGBT have a graphical user interface different to the text
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Now, when it comes to a different era of software, what's most interesting is about how it is different
from previous eras. One example he points out is that during Software 1.0, the early adopters
were governments and massive corporations because they were the only ones that could afford to
operate mainframes. A similar thing was true in Software 2.0, with neural networks largely the domain
of research labs and tech companies. This time, however, the everyday user was the first adopter
of LLMs and able to access this powerful new way to program a computer. He said,
it's really fascinating to me that we have a new magical computer, and it's helping me
boil an egg rather than helping the government with military ballistics. Indeed, corporations and
governments are lagging behind the adoption of all of us. His point is that this is completely unprecedented.
He continued, we all have a computer, it's all just software, and chat GPT was beamed down to billions
of people instantly and overnight. It's kind of insane to me that this is the case and now it's our
time to program these computers. Which is not to say that they are perfect. Indeed, with a new era of
software, we're finding new problems as well. There are, of course, the problems of hallucination and just
more generally jagged intelligence. In other words, while LLMs have perfect knowledge in some areas,
they can also then fail to be able to see how many R's are in the word strawberry.
Less discussed, though, is the idea that LLMs don't natively learn new things.
While a human working in an organization will learn how to perform specific tasks,
and LLM will forget everything as soon as the context window is closed.
This presents some very real limitations and breaks the analogy of human thought.
Carpathy said, you have to simultaneously think through this superhuman thing that has a bunch
of cognitive deficits and issues. Yet Carpathy also believes that there's an entire category of
computing tasks that are unlocked by LLMs that were only starting to scratch the surface of.
One of these ideas he called partial autonomy apps, or copilot or cursor for X.
The idea is an app like cursor, which acts as an overlay to LLMs and allows users to move faster.
Rather than talking to the LLM operating system directly, cursor can orchestrate many actions
with the human overseeing the process. He noted that these kinds of apps often have a feature he
referred to as an autonomy slider, where the user can select how much autonomy the LLM has to take
actions and make changes depending on how sensitive the task is. Carpathy, in fact, suggested that
most software will become partially autonomous, with some big implications for the software industry
who need to figure out how to integrate the new modality. He said, traditional software right now has
all these switches designed for humans, but that has the change to be accessible to LLMs.
One of the conclusions is that software should seek to make the feedback loop between LLM generation
and human verification as tight as possible. Carpathy is apparently interested in MCU references,
as he used the Ironman suit as a way to explain this autonomy slider idea. On one end of the
spectrum, there is Tony Stark wearing the suit versus when, a little bit down the line. He actually
built autonomous versions of the suit that could operate themselves. Carpathy said,
we can build augmentations or we can build agents, but we kind of want to do a bit of both.
At this stage, working with fallible LLMs, it's less building flashy demonstrations of autonomous
agents and more building partial autonomy products. And in one more example of the
the need for interfaces that connect the dots more fluidly between what semi-autonomous software is producing
and humans, he gave the example of vibe coding. As it stands at the moment, Carpathy said,
vibe coding is super great when you want to build something custom that doesn't appear to exist and
you just want to wing it, but he also walked through an app he has in production that transforms
restaurant menus into pictures for easy selection. He said, the code was actually the easy part.
Most of it was actually adding authentication and payments and a domain name. All of this was really hard.
It was me and a browser clicking stuff. I had the app working in a few hours, and then it took me a
because it was trying to make it real.
Bringing it all together, Carpathy argued that there's a new category of consumer that
needs infrastructure, saying, it used to be just humans through guis or computers through
APIs.
Agents are computers, but they're human-like.
There's people's spirits on the internet and they need to interact with our software infrastructure.
One example he gave of what it's going to look like to design for this audience is Versel and
Stripe, who allow LLMs to access their documentation via Markdown.
Carpathy said if we can make docs accessible to LLMs, it's going to unlock a huge
amount of use. And while accessibility is a big deal, the docs also need to fundamentally change
to reflect how an LLM will take actions. For Cell, for example, has already done this, replacing the
word click with agent-friendly API commands. Anthropics MCP is built on a similar concept. Carpathy said,
anytime your docs say click, this is bad, as an LLM won't be able to natively take this action right now.
The big takeaway is that there is still an absolute ton of code to be written to re-architect
the world of software for agents. The revolution in practice is about slowly an increment.
mentally moving the slider from augmentation to full automation, but the architecture buildout,
which Carpathie views as at least a decade long, has only just begun.
And so that is LRS for this week. Like I said, guys, I have barely scratched the surface on this
and would highly encourage you to go watch the whole video. For now, though, that is going to do it for
today's AI Daily Brief. I appreciate you listening or watching as always, and until next time,
peace.