The Best Code You Never Wrote
How AI is transforming software development
Last week, I published a brief LinkedIn post with some thoughts on AI code generation, in reaction to the staggering growth numbers reported about Cursor. Apparently, Cursor went from $100M to $300M ARR in four months, which is truly unprecedented growth at this scale. Since then, I’ve watched this excellent interview with Cursor’s CEO Michael Truell on Lenny’s Podcast, and wanted to follow up with a slightly extended and updated version.
1. Code Generation is THE B2B Killer App of Generative AI
It’s far from the only one, of course. Generative AI is impacting software across many verticals, and the ultimate gen AI killer app is ChatGPT. But in B2B, and for a relatively homogeneous use case (compared to ChatGPT, which, like Web search, is used for everything), code generation stands out.
Since writing that a week ago, I’ve come across some real data that prove it. ;-) If we take these (slightly outdated) UBS numbers and break it down into the major categories, we get this list:
As you can see, code generation represents more than 28% of what UBS includes under “AI Native Revenue”. If we combine image, video, and audio generation into media/creative, that category tops the list, accounting for more than 38% of the total. I’ve found it interesting to see that taken together, code generation and media/creative account for more than ⅔ of the total. As a caveat, this is based on data from only 21 companies, but I doubt that looking at the long-tail would radically change the picture.
2. It won’t take too long until most code will be written by AI
One weekend ago, I built a level editor in Unity for a game my son is working on. (He only lets me touch the plumbing, I’m not good enough for the really interesting parts 😄). Most of the C# code was AI-generated. I barely understood half of it, but it works.
That raises a big question that I’m sure many people have asked themselves lately: what happens if we increasingly rely on AI-generated code? Are we comfortable pushing code to production that no human being has reviewed or understood?
You could argue that this isn’t new. For C# to run on a computer, it must be translated into CPU instructions (machine code) via several intermediate steps. I remember back in the Commodore Amiga days, games (and what was called “demos”) were coded in pure assembler, which sounds (and I think was) crazy. We’ve since moved up the abstraction ladder, trading raw opcodes for C, Python or JavaScript. Maybe in a few years people will find it laughable to use a language like this because we’ll be talking to computers in plain English?
So abstraction isn’t new. Developers have always relied on compilers to generate lower-level code that no human ever reads. What’s different is that in the past, those compilers were deterministic and rule-based. What’s new is the probabilistic, opaque nature of LLMs.
For what it’s worth, I strongly believe that we’ll get there and that it won’t take decades. AI-generated code will earn our trust quickly — because AI will simply outperform human developers. Once AI codes better than almost any human, the dynamic will be similar to self-driving cars. Will autonomous vehicles make zero mistakes? No. But if they prevent 90% of accidents, we should get them ASAP.
3. Cursor, Midjourney, ElevenLabs: PLG on AI Steroids
Pre-PLG, a software company’s growth rate was usually constrained by the speed at which it managed to hire and train salespeople. Companies like Slack, Dropbox, and Zendesk showed that product-led growth can enable faster scaling. Now companies like Cursor, MidJourney, and ElevenLabs are taking this to the next level … what I like to call PLG on AI steroids.
None of these companies achieved their growth by building out a massive sales team. You can’t sell that fast. It has to be bought. And the speed of adoption is driven by the insane value that users get, enabled by AI. Traditional PLG usually came with a 30 day trial for setup, education, feature discovery, etc. Some of these new AI tools compress the entire onboarding and activation process into minutes.
In 2012, I wrote about how SaaS companies should make the learning curve as smooth as possible and give the user as much gratification along the way as possible. Just like game designers need to teach the game to new users in many small steps, meticulously making sure that it never gets too difficult nor boring, B2B software companies need to create a frictionless and rewarding onboarding experience.
With AI, it’s as if Mario has discovered steroids (or rocket fuel). The jumps are bigger. The payoffs come faster. And the wow moments hit instantly.
The question is: what other categories will see this dynamic emerge? Who else can turn foundational models into a product with such extreme pull?
4. Where Does the Value Accrue in Code Generation?
There’s been a lot of debate about where value lands in the AI stack. Will applications like Cursor win, or will the value flow down to the model providers (in addition to Nvidia)?
The bear case for Cursor, which started out as a rather thin UX layer, was that most of their revenue goes straight to Anthropic. That view seems outdated, given that Cursor is now training its own models, purpose-built for code. As Michael Truell said in the interview with Lenny: “We definitely didn’t expect to be doing any of our own model development. And at this point, every magic moment in Cursor involves a custom model in some way.”
Poolside, a P9 portfolio company, started there from day one — training an LLM that is entirely oriented towards software development and that improves by completing millions of tasks across tens of thousands of real world software projects (what Poolside calls Reinforcement Learning from Code Execution Feedback). Their foundational model, which can be fine-tuned on how customers write software, what libraries and APIs they use, etc. is designed to enable developers to produce the best code they never wrote.
So while Cursor and Poolside took very different paths — Cursor initially focusing on UX and generating momentum with PLG, Poolside focused on R&D and enterprise readiness — it seems that they are converging on the same view: if you want win in code generation, you have to deliver the best possible user experience and be able to make money even if margins decrease — and for that, you have to own a large part of the stack, including the model.
I’m generally not convinced by the “thin wrapper” narrative that dismisses apps as shallow UI layers on top of someone else’s model. Over the past few years, we’ve invested in multiple companies that solve real customer problems and build meaningful products without training their own models. But code generation may just be a special animal.
