Since we’re entering the area where AI is going to be writing most of the code, let’s take a moment to celebrate some of the people who are on the forefront of moving the ball forward.
First of all, it’s apparent that this is happening quickly. Various companies are talking about the proportions of their code that are currently written by AI, and these are increasing at a rapid clip. There’s a lot of job displacement already around this part of the tech world.
But certain personalities are basically becoming household names for their mastery of AI tools and processes, and their ability to see how this type of advancement shakes out.
And some of these pioneers are also sort of prophetic – they are on record talking about their outlooks, and people are listening.
Let’s go over some of what we’re hearing about the coding world in 2025 from these three innovators.
Andrej Karpathy
This former OpenAI member is well known for his work there and at Tesla in more recent years. He got a PhD at Stanford related to computer vision and NLP. He went on to found Eureka Labs, where students use the AI native platform to trainer and models.
His most famous quote?
“English is the hottest new programming language.”
This statement, on its own, addresses vibe coding directly, and describes the process by which coders become prompters. Since AI can create its own code, vibe coding is the practice of providing those broader prompts, seeing what comes out, and fine-tuning it for a final result.
Riley Brown
Riley Brown is big on YouTube, and prominent in the AI field. He actually founded a company called Vibe Code, which is a platform that can help developers to use AI tools to make their projects into reality.
People actually attribute the term “vibe coding” to Brown, who apparently coined it in emphasizing how to be creative and evolving applications over working with deterministic coding traditions.
His quote: “manually writing code is going to be a thing of the past.”
And that’s happening.
Tom Preston-Werner
Tom Preston-Werner (no direct relation, at least that I’m aware of) has a coding style that has been hailed as unique online, and is a cofounder of GitHub, the platform for code control and collaboration.
He also created Jeckyl, a converting tool for dynamic sites and blogs.
His quote: “You’re either one that creates the automation, or you’re getting automated.”
Podcasting Notes
It’s interesting to note that of these three, both Brown and Karpathy have been recently interviewed on one of my favorite podcasts, AI Daily Brief with Nathaniel Whittemore.
Specifically, Whittemore took time to read some quotes from a paper by Karpathy called “Power to the People,” in which the engineer notes some of the fundamental aspects of AI. Here are some quotes:
“Transformative technologies usually follow a top-down diffusion path: originating in government or military contexts, passing through corporations, and eventually reaching individuals – think electricity, cryptography, computers, flight, the internet, or GPS. This progression feels intuitive, new and powerful technologies are usually scarce, capital-intensive, and their use requires specialized technical expertise in the early stages. So it strikes me as quite unique and remarkable that LLMs display a dramatic reversal of this pattern – they generate disproportionate benefit for regular people, while their impact is a lot more muted and lagging in corporations and governments.”
“LLMs offer a very specific profile of capability – that of merely quasi-expert knowledge/performance, but simultaneously across a very wide variety of domains. In other words, they are simultaneously versatile but also shallow and fallible.”
“Looking forward, the continued diffusion of LLMs of course depends on continued performance improvement and its capability profile. The “benefit distribution” overall is particularly interesting to chart, and depends heavily on the dynamic range of the performance as a function of capital expenditure.”
There’s a lot more in the paper. These three quotes present some of the deeper points on LLM use. The first one shows the thesis (power to the people) and how LLMs differ from other forms of “top-down” technology. The second speaks to the specialization of models and how that is going to work. The third one contemplates cost, performance, and power.
So that’s a little bit of what you can take to the bank as we keep digesting the sudden integration of AI into our lives, in business and elsewhere. Remember: automation is coming.
