Just yesterday, I mentioned Andrej Karpathy, who made some waves with his recent X post talking about giving ground to AI agents to create software and write code.
Then I thought about one of our most influential voices in today’s tech world, MIT PhD Ethan Mollick, and I went over to his blog, One Useful Thing, to see if he was covering this new capability.
Sure enough, I found a March 11 piece titled “Speaking Things Into Existence” where Mollick covers this idea of “ex nihilo” code creation based on informal prompting.
The Hottest New Programming Language
In digging into this revolutionary use case, Mollick starts right up top with a quote from Karpathy that I think gets to the very heart of things – that “the hottest new programming language is English.”
Presumably, you could use other world languages, too, but so much of what happens in this industry happens in English, and hundreds of thousands of seasoned professionals are getting used to the idea that you can talk to an LLM in your own language, not in Fortran or JavaScript or C-sharp, but just in plain English, and it will come up with what you want.
Mollick tells us how he “decided to give it a try” using Anthropic‘s Claude Code agent.
“I needed AI help before I could even use Claude Code,” he said, citing the model’s Linux build as something to get around. Here, Mollick coins the phrase “vibetroubleshooting”, and says “if you haven’t used AI for technical support, you should.”
Asking AI to Make Applications
“Time to vibecode,” Mollick wrote, noting that his first prompt to Claude Code was: “make a 3-D game where I can place buildings of various designs, and then drive through the town I create.”
“Grammar and spelling issues included,” he disclaims, “I got a working application about four minutes later.”
He then illustrates how he tweaked the game and solved some minor glitches, along with additional prompts like:
“Can you make buildings look more real? Can you add in a rival helicopter that is trying to extinguish fires before me?”
What it Costs
He then provides the actual cost for developing this new game – about $5.00 to make the game, and $8.00 to fix the bug.
“Vibecoding is most useful when you actually have some knowledge and don’t have to rely on the AI alone,” he adds. “A better programmer might have immediately recognized that the issue was related to asset loading or event handling. And this was a small project… This underscores how vibecoding isn’t about eliminating expertise but redistributing it – from writing every line of code to knowing enough about systems to guide, troubleshoot, and evaluate. The challenge becomes identifying what ‘minimum viable knowledge’ is necessary to effectively collaborate with AI on various projects.”
People and Processes
“Expertise clearly still matters in a world of creating things with words,” Mollick continues. “After all, you have to know what you want to create; be able to judge whether the results are good or bad; and give appropriate feedback.”
On the part of the machines, he refers to a “jagged frontier” of capabilities.
That might be fair, but the idea that humans are there for process refinement and minor tweaking is sort of weak tea compared to the staggering capability of these machines to do the creative work. How long until model evolution turns that jagged edge into a spectacular smooth scalpel?
DeepSeek All Over Again?
At the same time that we’re trying to digest all of this, there’s another contender in the ring. A bit later in the blog, Mollick references Manus, a new Chinese AI agent that uses Claude and other tools for fundamental task management.
Mollick details how he asked Manus to “create an interactive course on elevator pitching using the best academic advice.”
“You can see the system set up a checklist of tasks and then go through them, doing web research before building the pages,” he says. “As someone who teaches entrepreneurship, I would say that the output it created was surface-level impressive – it was an entire course that covered much of the basics of pitching, and without obvious errors! Yet, I also could instantly see that it was too text heavy and did not include opportunities for knowledge checks or interactive exercises.”
Here, you can see that the system is able to source the actual content, the ideas, and then arrange them and present them the right way. There’s very little human intervention or work needed. That’s the reality of it.
We just had the Chinese announcement of DeepSeek tanking stocks like Nvidia. What will Manus do? How does the geopolitical interplay of China and the U.S. factor into this new world of AI software development?
That question will be answered pretty soon, as these technologies make their way to market.
As for Mollick, he was also able to dig up old spreadsheets and get new results with the data-crunching power of AI.
“Work is changing, and we’re only beginning to understand how,” Mollick writes. “What’s clear from these experiments is that the relationship between human expertise and AI capabilities isn’t fixed. …
The current moment feels transitional. These tools aren’t yet reliable enough to work completely autonomously, but they’re capable enough to dramatically amplify what we can accomplish.”
There’s a lot more in the blog post – you should read the whole thing, and think about the work processes that Mollick details. On a side note, I liked this response from a poster named “Kevin” that talks about the application to teams culture:
“To me, vibecoding is similar to being a tech lead for a bunch of junior engineers,” Kevin writes. “You spend most of your time reviewing code, rather than writing code. The code you review is worse in most ways than the code you write. But it’s a lot faster to work together as a team, because the junior engineers can crank through a lot of features. And your review really is important – if you blindly accept everything they do, you’ll end up in trouble.”
The New World of Work
Taking this all in, in the context of what I’ve already been writing about this week, it seems like many of the unanswered questions have to do with human roles and positions. Everything that we used to take for granted is changing suddenly. How are we going to navigate this? Can we change course quickly enough to leverage the power of AI without becoming swamped in its encompassing power?
Feel free to comment, and keep an eye on the blog as we head toward some major events in the MIT community this spring that will have more bearing on what we’re doing with new models and hardware setups.
