'Nuclear Launch detected'.
'Nuclear Launch detected'.
The difference is that in gambling 'the house always wins', but in our case we do make progress towards our goal of conquering the world with our newly minted apps.
The situation where this comparison holds is when vibe coding leads nowhere and you don't accomplish anything but just burn through tokens.
Software engineers work on Jira tickets, created by product managers and several layers of middle managers.
But the power of recent models is not in working on cogs, their true power is in working on the entire mechanism.
When talking about a piece of software that a company produces, I'll use the analogy of a puzzle.
A human hierarchy (read: company) works on designing the big puzzle at the top and delegating the individual pieces to human engineers. This process goes back and forth between levels in the hierarchy until the whole puzzle slowly emerges. Until recently, AI could only help on improving the pieces of the puzzle.
Latest models got really good at working on the entire puzzle - big picture and pieces.
This makes human hierarchy obsolete and a bottleneck.
The future seems to be one operator working on the entire puzzle, minus the hierarchy of people.
Of course, it's not just about the software, but streams of information - customer support, bug tickets, testing, changing customer requirements.. but all of these can be handled by AI even today. And it will only get better.
This means different things depending on which angle you look at it - yes, it will mean companies will become obsolete, but also that each employee can become a company.
It is highly unlikely that the hardware which makes LLMs possible would have been developed otherwise.
Isn't that amazing ?
Just like internet grew because of p*rn, AI grew because of video games. Of course, that's just a funny angle.
The way I see it, AI isn't accidental. Its inception has been in the first chips, the Internet, Open Source, Github, ... AI is not just the neural networks - it's also the data used to train it, the OSes, APIs, the Cloud computing, the data centers, the scalable architectures.. everything we've been working on over the last decades was inevitably leading us to this. And even before the chips, it was the maths, the physics ..
Singularity it seems, is inevitable and it was inevitable for longer than we can remember.
There's this interesting arc of growth for apps which are successful. At first users love it, company grows, founders get rich, they hire expensive people to develop the product and increase revenue until eventually the initial culture and mission is replaced by internal politics and processes.
Software starts getting features which users don't want or need, side effects of the company size and their Q4 roadmap to 'optimize' revenue|engagement|profits|growth|...
Users become tools in the hands of the app they initially used as a tool. This model worked well so far and built some of the biggest companies in history.
AI could make this business model less effective. Once a piece of software becomes successful and veers off into crap territory, people will start cloning it, keeping only the features that made that software successful initially. Companies who try to strong arm their users will see users jump ship, or rather, de-board on islands.
At least I hope this will be the case.
From the tools which were used to design and develop the models (programming languages, libraries) to the operating systems running them to the databases used for storing training data .. plus of course they were trained mostly on open source code.
If OSS didn't exist, it's highly unlikely that LLMs would have been built.
Where `:people` is a key in a huge (larger than memory) map. This database will only touch the referenced nodes when traversing, without loading the whole thing into memory.
So the 'query language' is actually your programming language. To the programmer this database looks like an in-memory data structure, when in fact it's efficiently reading data from the disk. Plus immutability of course (meaning you can go back in history).
What's the play after you have automated yourselves out of a job?
Retrain as a skilled worker? Expect to be the lucky winner who is cahoots with the CEO/CTO and magically gets to keep the job? Expect the society to turn to social democracy and produce UBI? Make enough money to live off investments portfolio?
I had to stop doing this because it greatly slowed down and confuse the model, when it did a repo search and found results in some old md files. Plus token usage went through the roof. So keeping changes in the open like that in the repo doesn't work.
Not sure how tfa works, but hopefully the model doesn't see that data.