The Inevitable Collapse of AI Code Generators

According to GitHub, there are roughly 500 actively used programming languages on earth. If you check the list, it does not specify English as a programming language. I suspect they might have to do this soon though. To understand why, consider this piece of text.

HTTP endpoint that allows for deleting an item in todo table in logistics database.

Primary key of table is todo_id

Can only be executed by admin users.

The above is literally an example of "source code" resulting in working applications thx to our Hyperlambda Generator. If you don't understand what I'm talking about, please watch this video.

If you watch the above video, you will realise that we're soon at the point where we can upload entire specifications, and have complete backend systems automatically generated using AI and LLMs.

The specification IS the system!

Imagine holding a sales meeting for a client needing an API or some sort of backend, wrapping an existing database. When the meeting started, you turned on some sort of summarise with AI thing. At the end of the meeting, you're getting a complete summary of everything discussed in the meeting. You upload the summary to a pre-processor AI agent, that's compressing it down to a handful of Hyperlambda file specifications. You click the button, and 5 minutes later you've got 50 HTTP API endpoints solving every problem from your specification.

If you had told me I'd be working with stuff like this 5 years ago, I'd say you were lying. Today it's what everybody is trying to do, including Google with Firebase, Microsoft, Cursor, Manus AI, OpenAI, Anthropic, etc, etc, etc. However, we do have one "unfair advantage", allowing us to move faster in this space than these behemoth companies.

It's the language duh!

AI might never be able to perfectly create complex software systems without hallucinating and creating bugs. This is because of that "modern" programming languages (they're all 30 years old) are not created with the intention being to automatically generate code using AI. Instead, they're created with dozens of unnecessary abstractions, intended to make our lives eaiser as human beings, while reading and maintaining code.

Hyperlambda however is created as a declarative homo-iconic programming language, with meta-programming constructs everywhere. Meta programming again, literally translates to "creating software that creates software". The fact that NASDAQ behemoths don't understand this simple fact is puzzling for me personally - But I'm not complaining, I suspect they'll panick once they actually understand that what I'm talking about, at which point it'll probably be too late for them to catch up with me.

You see, modern programming languages are simply not created to be able to "generate code" using AI. To illustrate the problem, below is a piece of snippet I copied from GitHub illustrating how to perform an HTTP invocation using HttpClient in C#.

namespace API.Controllers
{
    public class GithubController : ApiController
    {
        private const string _address = "https://api.github.com/users/tdshipley";
        private const string _userAgent = "TestApp";

        // GET api/
        public async Task Get()
        {
            var result = await GetAsync(_address);

            return result.ToString();
        }

        private async Task GetAsync(string uri)
        {
            HttpClient client = new HttpClient();

            client.DefaultRequestHeaders.Add("Authorization", "token ADD YOUR OAUTH TOKEN");
            client.DefaultRequestHeaders.Add("User-Agent", _userAgent);
            var content = await client.GetStringAsync(uri);

            return JObject.Parse(content);
        }
    }
}

The above was the second match I could find from GitHub when searching. Ignoring the fact that the above code has at least 3 bugs, including a bug that might potentially crash your server (hint; It's leaking socket connections) - It kind of resembles Greek to most people I would assume. Below is the equivalent piece of code using Hyperlambda.

http.get:"https://api.github.com/users/tdshipley"
   headers
      Authorization:token ADD YOUR OAUTH TOKEN
      User-Agent:TestApp

Can you see the difference? If you can't see the problem, let's count how many tokens our examples have.

• C# code has 338 tokens
• Hyperlambda code has 52 tokens

This implies the C# code snippet will consume 6.5 times as many tokens, implying 6.5 times as much electricity during inference, and require a 6.5 times larger model to be able to deliver the same quality. According to Facebook's CEO, it seems we've reached the ceiling when it comes to improving LLMs with "brute force". If this is true, it means it's impossible to simply throw more hardware and larger models at the problem to solve it, and that the only long term solution for solving the problem, is to fundamentally change how LLMs are producing code. Implying ...

We'll need an AI Programming Language to continue scaling up

Hyperlambda was created as a homo-iconic programming language, declarative in nature, with meta-programming contructs "all over the place". This means that instead of asking ourselves how to create a better and faster programming language, we asked ourselves how to create a programming language specialised for AI code-generators.

Hyperlambda is an "AI first" programming language

This allows us to get away with a lot of things these behemoth companies can't get away with, such as using GPT-4.1-mini instead of larger models to generate Hyperlambda, and still getting high quality code as our result. A guesstimate would be that to generate a backend web API using Firebase probably spends somewhere between 20 to 50 times more electricity than generating Hyperlambda solving the same problem. Implying when you're generating an app using AI with Magic, you're using electricity worth maybe 50 cents. When you're generating the same language in C# using Claude, Cursor, or Firebase, you're spending maybe $25 worth of electricity. Google and Microsoft might have deep pockets, but they're not infinite ...

In addition, once you realise that tokens are your context window size, it's easily understood that you can also create applications that are 6.5 times larger, because the maximum token window is fixed, and our LLM has the same context window size as Google's LLMs. Implying you can just send ALL files in a Magic Cloud app to the LLM at once, for an app that is 6.5 times larger than a similar C# app. However, even that is almost irrelevant, because Hyperlambda and Magic was created to have "perfect encapsulation", where each individual code file is the whole code!

To understand the problem with traditional languages, realise that a standard C# solution doing some HTTP invocations typically need to transmit some 25 files to the LLM to edit a simple HTTP GET invocation method. This is a compounding problem originating from the need to have these "abstractions", complicating the code. With abstractions I mean AutoMapper, View Models, Data Models, Dependency Injection Configurations, mapping configurations, etc, etc, etc. Before the LLM knows your code, it's been forced to read through 25 files of "abstractions". With Hyperlambda it's just that simple file, consuming 52 tokens, and that's ALL OF IT!

This implies that token count now further increases by a multiplication fact of 25. We're now looking at the following figures.

• C# solution, 8,450
• Hyperlambda solution, still at 52 tokens in total

That is a ratio of 162.5, implying if you spend enectricity worth $10 generating a Hyperlambda application, you'd need to spend $1,625 to generate the same C# solution.

This problem is not a new problem, we've always had it as human beings, resulting in that these "abstractions" required to make the code "readable and maintainable" is actually everything BUT! You might not care about Hyperlambda, and prefer a C# solution, or a Python solution, or a GoLang solution, etc - But the LLM doesn't care. And for a newbie software developer who's never seen code in his life, I can pretty much guarantee you that he'll prefer reading Hyperlambda and not C#. And noobs are flocking to "vibe coding" today as if it was the holy grail.

Over time I suspect this will dawn for NASDAQ companies, at which point they'll either try to buy me (I'm not for sale), or create their own homo-iconic programming language - However, at that time it might be too late for them, because they've invested too much time and effort into their existing efforts, in addition to having laid off most people who actually understand how to build a programming language.

I personally don't care, I've been screaming from the top of my lungs about this for more than a decade, yet still nobody listens to me. However, I only need one person to listen to me, allowing me to teach, and that person's name is "GPT-4.1-mini", and it's got a 1 million context tokens window size - Implying you can at least in theory create applications that are 162.5 times "larger" with Hyperlambda than whatever you can create with Cursor, Manus, and Firebase. And since these companies are fighting with each other to acquire networking effects, I can fine tune GPT-4.1 for $5 instead of $500,000, which would probably have been the "real cost" if it wasn't for OpenAI needing to give away free tokens to attract users to gain networking effects, and I'd have to create my own LLM ...

When will they ever learn? Never I suspect! The difference is that I don't care anymore. They simply need to go on their noses a couple of times to get it ...

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