"Time to kick off that new priority in your company!"
Web developer, mainly frontend and application logic, with basic infra and backend skills to get my work online.
Examples:
AI systems from scratch (no Langchain and similar), Interactive app screens, PDF generators, payment processing, maps and routes, VoIP, browser extensions, SFTP server client and more.
Stack: SvelteKit, Microsoft Azure functions, Netlify functions, AWS Lambda functions, Node, Docker, Appwrite as a full backend (Supabase alternative), Caddy as a front door (Nginx alternative).
There will always be someone whose job is to program computers to do things.
That's us, developers.
That will never change.
We're the ones dedicated to it.
Execs, managers, HR, salesmen, designers etc won't suddenly want to spend their whole days, not even half of their time, tinkering with a computer so it can do what they want.
Else Basic and Fortran would have made everyone software developers.
Exactly this. Sora 2.0 came out! It's amazing. I spent an evening with it and got bored. The amazing limitless potential of it blows my mind. But other than a couple of random attempt, thats simply not where my heart lies.
My Claude Code usage is through the roof, however.
This is exactly how I felt when Stable Diffusion came out in 2023. It turns out I am not an artist and eventually got bored by it whereas actual artists used it for hours, the same as we engineers use LLMs. The personality does not change, only the tool.
Yes, there will be always someone who is needed to program stuff. Totally agree with that.
But my question is "how many of those will be needed", because I am not saying that programmers are not needed.
When less numbers are needed, there will be so much competition in finding those jobs, esentially would also mean not able to find the work, as there will be always someone who would be willing to the job at lower wage and come to work with more youthful energy.
I've had a long career, and seen a number of systemic changes.
I've lived through two software "explosions" where minimal skills lead to large output. The first was web sites and the second was mobile.
Web sites are (even now) pretty easy. In the late 90's though, and early 2000's there was tremendous demand for web site creation. (Every business everywhere suddenly needed a web presence.) This lead to a massive surge in building-web-site training. No time for 3 year degree, barely time for 90 days of "click here, drag that".
So there was this huge percentage of "programmers" that had a very shallow skill set. When the bubble burst it was this group that bore the brunt.
Fast forward to 2007, and mobile apps become a "thing". Same pattern evolves, fast training, shallow understanding, apps do very little (most of the heavy lifting, if it exists at all, is on the backend.) Not a lot of time spent on UI or app flow etc.
This time around the work is also likely to be done offshore. Turns out simple skills can be taught anywhere, tiny programs can be built anywhere.
Worse, management typically didn't understand the importance of foundations like good database design, coherent code, forward thinking, maintainence etc. Programs are 10% creation, 90% maintainence (adding stuff, fixing stuff etc.) From a management point of view (and indeed from those swathes of shallow practioners) the only goal is "it works."
AI is this new (but really old) idea that shallowness is sufficient. And just like before it first replaces people who themselves have only shallow skills; who see "coding" as the goal of their job.
We are far from the end of this cycle, and who knows where it will go, but yes, those with shallow skills are likely to be first on the chopping block.
Those with better foundations (a better understanding of good and bad, perhaps with a deeper education, or deeper experience) and the ability to communicate that value to management are positioned well.
In other words, yes the demand for "lite" developers will implode. But at the same time demand for quality devs, who can tell good from bad (design, code, ui etc) goes up.
If you are a young graduate, you're going to be light on experience. If you're and older person, but had very shallow (or no) training you're easily replaced. If you think development is code, you're not gonna do well.
In truth development is not about code (and never has been). It's about all the processes that lead up to the code. Where possible (even at college level) try and focus on upskilling on "big picture" - understanding the needs of a business, the needs of the customer, the architecture and design that results in "good" or "bad".
AI is a tool. It's important to understand when it's doing good, but also when it's doing bad.
> AI is this new (but really old) idea that shallowness is sufficient.
That’s not the whole story and certainly not the core concern, which is more about developers who already have deep experience, using AI to multiply their output.
You've seen the "Dot-com" and "Mobile" cycles. This "AI cycle" feels faster, but the trap is the same: Mistaking Access for Mastery.
In Japanese martial arts, we have "Shuhari" (Obey, Digress, Separate).
AI gives everyone a shortcut to the final stage ("Look, I made an app!"), skipping the painful "Obey" stage where you learn why things break.
As you said, when the bubble bursts, only those who understand the "Foundation" (database design, consistency) will remain standing. The tools change, but the physics of complexity do not.
It's not a zero sum game. Think, an agronomist visits a farm, instructs to cut a certain plant for the animals to eat at a certain height instead of whenever, the plant then provides more food for the animals to eat exclusively due to that, no other input in the system, now the animals are cheaper to feed, so more profit to the farmer and cheaper food to people.
It would be if demand was limited. Let's assume the people already have enough food, and the population is not growing - that was my premise. Through innovation, one farmer can grow more than all the others.
Since there already was enough food, the market is saturated, so it would effectively reduce the price of all food. This would change the ratio so that the farmer who grows more gets more money in total, and every other farmer gets a bit less.
As long as there is any sort of growth involved - more people, more appetite, whatever, it would be value creation. But without growth, it's not.
At least not in the economical sense. Saving resources and effort that goes into producing things is great for society, on paper. But with the economic system that got us this far, we have no real mechanism for distributing the gains. So we get over supplying producers fighting over limited demand.
The world is several orders of magnitude more complex than that example, of course. But that's the basic idea.
That said, I'm not exactly an economist, and considering it's a bleak opinion to hold, I'd like to learn something based on which I could change it.
Late comment but if technology brought down the price of food then people could spend less on food, more on other good and services. Or the same on higher quality food. You don't need an increasing population for that. The improvement in agriculture could mean some farmers would have to find other work. So you can have economic growth with a stagnant or falling population. And you can rather easily have economic growth on a per-capita basis with no overall GDP growth, like is common in Japan today.
About the farmer needing to change jobs, in the interview that is the subject of this thread Ilya Sutskever speaks with wonder about humans' ability to generalize their intelligence across different domains with very little training. Cheaper food prices could mean people eat out or order-in more and then some ex-farmers might enter restaurant or food preparation businesses. People would still be getting wealthier, even without the tailwind of a growing population.
For JSON I agree, now I can just mention JSON and provide examples and the response always comes in the right format, but for tool calling and information retrieval I have never seen a system actually work, nor in my tests have these worked.
Now, I'm open to the idea that I am just using it wrong, but I have seen several reports around the web that the most that people got in tool calling accuracy is 80%, which is unusable for any production system, also for info retrieval I have seen it lose coherence the more data is available overall.
Is there a model that actually achieved 100% tool calling accuracy?
So far I built systems for that myself, surrounding the LLM, and only like this it worked well in production.
There is an experiment in cows in which they swapped the cow's rumen contents, which is a big gut with way more microorganisms than the regular gut, and their microbiomes reverted back to their original profiles, which is different to these findings in mice here.
I remember hearing about that rumen swap experiment. One of my professors at the time said that more substantial changes could be affected by doing repeat rumen content swaps.
You're totally right, "protocol" is way too heavy.
A simple convention like aidata.json is perfect. That's the "win-win" I was looking for: the site gets to clearly offer what it wants the AI to see, and the AI gets clean data instead of having to guess at brittle HTML.
Fully agree. I personally had clients backing off from entire projects due to not being able to ship some native like experience on iOS, only Android, for example the prompt to install as a progressive web app.
I'm tired of losing sales due to that. And clients needing to increase their costs massively due to that.
Remote: Yes
Location: Brazil
Willing to relocate: No (Remote only)
Technologies: Custom AI systems from scratch, SvelteKit, Azure functions, Netlify functions, AWS Lambda, Node, Docker, Appwrite, Caddy, HTML, CSS, JS.
Generalist web developer.
Email: [email protected]
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