I just made a LLM recreate a decent approximation of the file system browser from the movie Hackers (similar to the SGI one from Jurassic park) in about 10 minutes. At work I've had it do useful features and bug fixes daily for a solid week.
Something happened around newyears 2026. The clients, the skills, the mcps, the tools and models reached some new level of usefulness. Or maybe I've been lucky for a week.
If it can do things like what I saw last week reliably, then every tool, widget, utility and library currently making money for a single dev or small team of devs is about to get eaten. Maybe even applications like jira, slack, or even salesforce or SAP can be made in-house by even small companies. "Make me a basic CRM".
Just a few months ago I found it mostly frustrating to use LLM's and I thought the whole thing was little more than a slight improvement over googling info for myself. But the past week has been mind-blowing.
Is it the beginning of the star trek ship computer? If so, it is as big as the smartphone, the internet, or even the invention of the microchip. And then the investments make sense in a way.
The problem might end up being that the value created by LLMs will have no customers when everyone is unemployed.
I also have the same experience where we rejected a SAP offering with the idea to build the same thing in-house.
But... aside from the obvious fact that building a thing is easier than using and maintaining the thing, the question arose if we even need what SAP offered, or if we get agents to do it.
In your example, do you actually need that simple CRM or maybe you can get agents to do the thing without any other additional software?
I don't know what this means for our jobs. I do know that, if making software becomes so trivial for everyone, companies will have to find another way to differentiate and compete. And hopefully that's where knowledge workers come in again.
And if you can be so productive, then where exactly do we need this surplus productivity in software right now when were no longer in the "digital transformation" phase?
Now if the only thing I was doing was writing code to a specification written by someone else, then I would be scared, but in my quarter century career that has never been the case. Even at my first job as a junior web developer before graduating college, there was always a conversation with stakeholders and I always had input on what was being built. I get that not every programmer had that experience, but to me that's always been the majority of the value that software developers bring, the code itself is just an implementation detail.
I can't say that I won't miss hand-crafting all the code, there certainly was something meditative about it, but I'm sure some of the original ENIAC programmers felt the same way about plugging in cables to make circuits. The world of tech moves fast, and nostalgia doesn't pay the bills.
True, but whether all those problems are SEEN worth chasing business wise is another matter. Short term is what matters most for individuals currently in the field, and short term is less devs needed which leads to drop in salaries and higher competition. You will have a job but if you explore the job market you will find it much harder to get a job you want at the salary you want without facing huge competition. At the same time, your current employer might be less likely to give you salary raises because they know you bargaining power has decreased due to the job market conditions.
Maybe in 40 years time, new problems will change the job market dynamics but you will likely be near retirement by then
We had a fresh out of school EE hire who left our company for an SWE position 6 months into his job with us, for a position that paid the same (plus full remote with a food stipend) as our Director of Engineering. A 23 yr old getting on offer above what a 54 yr old with 30 years experience was making.
For a few years there, you had to be an idi...making sub-optimal decisions, to choose anything other than becoming an techy.
And what happens then? Will we stop using each others code?
How long until that the devs at that major corporation start using an LLM? You think your smaller team can still compare to their huge team?
There’s some quality issues - I think some of the tests are slightly wrong. We went back and forth on some ambiguities Claude found in the spec, and how we should actually interpret what the jmap spec is asking. But after just a day, it’s nearly there. And it’s already very useful to see where existing implementations diverge on their output, even if the tests are sometimes not correctly identifying which implementation is wrong. Some of the test failures are 100% correct - it found real bugs in production implementations.
Using an AI to do weeks of work in a single day is the biggest change in what software development looks like that I’ve seen in my 30+ year career. I don’t know why I would hire a junior developer to write code any more. (But I would hire someone who was smart enough to wrangle the AI). I just don’t know how long “ai prompter” will remain a valuable skill. The AIs are getting much better at operating independently. It won’t be long before us humans aren’t needed to babysit them.
My spider sense is tingling!
> Build a thorough test suite for JMAP in this directory. The test suite will be run against multiple JMAP servers, to ensure each server implements the JMAP spec consistently and correctly. In this directory are two files - rfc8620.txt and rfc8621.txt. These files containing the JMAP core and JMAP email specs. Read these files. Then make a list of all aspects of the specifications. For each, create a set of tests which thoroughly tests all aspects of a JMAP server's behaviour specified by the RFCs, including error behaviour. The test suite should be configurable to point at a jmap server & email account. The account will contain an empty mailbox (error if its not empty). The test suite starts by adding a known set of test emails to the account, then run your tests and clear the inbox again. Write the test suite in typescript. The test runner should output the report into a JSON file. Start with a project plan.
If you haven't tried claude code or openai's codex, just dive in there and give it a go. Writing a prompt isn't rocket science. Just succinctly say the same things you'd say to a very competent junior engineer when you want to brief them on some work.
I'm not a professional programmer, but I am the I.T. department for my wife's small office. I used ChatGPT recently (as a search engine) to help create a web interface for some files on our intranet. I'm sure no one in the office has the time or skills to vibe code this in a reasonable amount of time. So I'm confident that my "job" is secure :)
the thing you are describing can be vibe coded by anyone. Its not that teachers or nurses are gonna start vibecoding tmrw, but the risk comes from other programmers outworking you to show off to the boss. Or companies pitting devs against each other, or them mistakenly assuming they require very few programmers, or PMs suddenly start vibe coding when threatened for their jobs.
What does that tell me?
It tells me that I shouldn't waste my time with a tool that's going to fundamentally change in three to six months; that I should wait until I stop hearing stories like this for a good, long while. "But you're going to be left behind!", yeah, maybe. But. I've been primarily a maintenance programmer for a very long time. The "bleeding edge" is where I am very, very rarely... and it seems to work out fine.
New tools that are useful are nice. Switching to a radically different tool every quarter or two? Not nice. I've got shit to do.
Sure, there will probably be some changes around MCP, skills, AGENTS.md and similar, but I don't see them as big changes, and you can use the tools now without those things.
This is as insightful as a fellow noting that both a caulk gun and a shotgun have a fixed handle and movable trigger and genuinely wondering why an expert user of the former would ever have even a moment's trouble learning to use the latter.
Regardless..
> The problem might end up being that the value created by LLMs will have no customers when everyone is unemployed.
This mentality is why investors are scrambling right now. It’s a scare tactic.
This is a wrong way to look at it. The right way is to consider that AI investments generate (taxable) economic activity that your government can use to build "hospitals, roads, houses, machine shops, biomanufacturing facilities, parks, forests, laboratories".
> How many hospitals, roads, houses, machine shops, biomanufacturing facilities, parks, forests, laboratories, etc. could we build with the money we’re spending on pretraining models that we throw away next quarter?
It's about using the money for to build things that we actually need and that have more long term utility. No one expects someone with a 100M signing bonus at Meta to lay bricks, but that 100M could be used to buy a lot of bricks and pay a lot of brick layers to build hospitals.
“We?”
This isn’t “our” money.
If you buy shares, you get a voice.
As for the rest, constraint on hospital capacity (at least in some countries, not sure about the USA) isn't money for capex, it's doctors unions that restrict training slots.
These models are vast and, in many ways, clearly superhuman. But they can't venture outside their training data, not even if you hold their hand and guide them.
Try getting Suno to write a song in a new genre. Even if you tell it EXACTLY what you want, and provide it with clear examples, it won't be able to do it.
This is also why there have been zero-to-very-few new scientific discoveries made by LLM.
Last time I checked the chips are not rewiring themselves like the brain does, nor does even the software rewrite itself, or the model recalibrate itself - anything that could be called "learning", normal daily work for a human brain.
Also, the models are not models of the world, but of our text communication only.
Human brains start by building a model of the physical world, from age zero. Much later, on top of that foundation, more abstract ideas emerge, including language. Text, even later. And all of it on a deep layer of a physical world model.
The LLM has none of that! It has zero depth behind the words it learned. It's like a human learning some strange symbols and the rules governing their appearance. The human will be able to reproduce valid chains of symbols following the learned rules, but they will never have any understanding of those symbols. In the human case, somebody would have to connect those symbols to their world model by telling them the "meaning" in a way they can already use. For the LLM that is not possible, since it doesn't habe such a model to begin with.
How anyone can even entertain the idea of "AGI" based on uncomprehending symbol manipulation, where every symbol has zero depth of a physical world model, only connections to other symbols, is beyond me TBH.
I don't think that's true anymore, though. All the SOTA models are multimodal now, meaning that they are trained on images and videos as well, not just text; and they do that is precisely because it improves the text output as well, for this exact reason. Already, I don't have to waste time explaining to Claude or Codex what I want on a webpage - I can just sketch a mock-up, or when there's a bug, I take a screenshot and circle the bits that are wrong. But this extends into the ability to reason about real world, as well.
You also need more than one simple brain structure simulation repeated a lot. Our brains have many different parts and structures, not just a single type.
However, just like our airplanes do not resemble bird flight as the early dreamers of human flight dreamed of, with flapping wings, I also do not see a need for our technology to fully reproduce the original.
We are better off following our own tech path and seeing where it will lead. It will be something else, and that's fine, because anyone can create a new human brain without education and tools, with just some sex, and let it self-assemble.
Biology is great and all but also pretty limited, extremely path-dependent. Just look at all the materials we already managed to create that nature would never make. Going off the already trodden bio-path should be good, we can create a lot of very different things. Those won't be brains like ours that "Feel" like ours, if that word will ever even apply. and that's fine and good. Our creations should explore entirely new paths. All these comparisons to the human experience make me sad, let's evaluate our products on their own merit.
One important point:
If you truly want a copy, partial or full, in tech, of the human experience, you need to look at the physics. Not at some meta stuff like "text"!!
The physical structure and the electrical signals in the brain. THAT is us. And electrical signals and what they represent in chips are so completely and utterly different from what can be found in the brain, THAT is the much more important argument against silly human "AGI" comparisons. We don't have a CPU and RAM. We have massively parallel waves of electrical signals in a very complex structure.
Humans are hung up on words. We even have fantasy stories hat are all about it. You say some word, magic happens. You know somebody's "true name", you control them.
But the brain works on a much lower deeply physical level. We don't even need language. A human without language and "inner voice" still is a human with the same complex brain, just much worse at communication.
The LLMs are all about the surface layer of that particular human ability though. And again, that is fine, but it has nothing to do with how our brains work. We looked at nature and were inspired, and went and created something else. As always.
I'd add that fiction is much more complicated. LLMs can clearly write original fiction, even if they are, as yet, not very good at it. There's an idea (often attributed to John Gardner or Leo Tolstoy) that all stories boil down to one of two scenarios:
> "A stranger comes to town."
> "A person goes on a journey."
Christopher Booker wrote that there are seven: https://en.wikipedia.org/wiki/The_Seven_Basic_Plots
So I'd tentatively expect tomorrow's LLMs to write good fiction along those well-trodden paths. I'm less sanguine about their applications in scientific invention and in producing original music.
Intelligence is mostly about pattern recognition. All those model weights represent patterns, compressed and encoded. If you can find a similar pattern in a new place, perhaps you can make a new discovery.
One problem is the patterns are static. Sooner or later, someone is going to figure out a way to give LLMs "real" memory. I'm not talking about keeping a long term context, extending it with markdown files, RAG, etc. like we do today for an individual user, but updating the underlying model weights incrementally, basically resulting in a learning, collective memory.
I am not at all sure that the same thing is even theoretically possible for LLMs.
Not to be facetious, but you need to spend more time playing with Suno. It really drives home how limited these models are. With text, there's a vast conceptual space that's hard to probe; it's much easier when the same structure is ported to music. The number of things it can't do absolutely outweighs the number of things it can do. Within days, even mere hours, you'll become aware of its peculiar rigidity.
For us to take the next step towards AGI, we need an AI winter to hit and the next AI summer to start, the first half of which will produce the advancement we actually need