it will use a lot of energy for every query and a lot of resources for cooling

Well, so do coders. Coffee can be quite taxing on the environment, as can air conditioning!

And just like that, they’ll forget about these previous statements as well.

I fear Elon Musk’s broken promises method is being admired and copied.

We’ll have full self driving next year.

Will the matrix it puts us in be in 1999? Because I’d take that deal.

SHUT UP AND GO BACK TO OUR SHITTY YAML BASED INFRASTRUCTURE!

🤣😂😪😥😢😭

😜 👢

Define “works.”

Because the goals of a money-hungry CEO don’t always align with those of the workers in the company itself (or often, even the consumer). I imagine this guy will think it worked just fine as he’s enjoying his golden parachute.

Oh perhaps the CEOs are the ones that need to be replaced?

Like in Twitter?

Define “works”?

If you’re a CEO, cutting all your talent, enshittifying your product, and pocketing the difference in new, lower costs vs standard profits might be considered as “working”.

job wasn’t that important

I keep telling you that changing out the battery in the smoke alarm isn’t worth the effort and you keep telling me that the house is currently on fire, we need to get out of here immediately, and I just roll my eyes because you’re only proving my point.

Nah, if it doesn’t pan out then all these folks will pretend they never said this, but in 24 months programming will be obsoleted by <insert fresh buzz here>

It also doesn’t know what’s going on a couple line before it, so say I am in a language that has options for functional styling using maps and I want to keep that flow going, it will start throwing for loops at you, so you end up having to rewrite it all anyway. I have find I end up spending more time writing the prompts then validating it did what I want correctly (normally not) than just looking at the docs and doing it myself, the bonus being I don’t have to reprompt it again later because now I know how to do it

It’s based on the last few years of messaging. They’ve consistently said AI will do X, Y, and Z, and it ends up doing each of those so poorly that you need pretty much the same staff to babysit the AI. I think it’s actually a net-negative in terms of productivity for technical work because you end up having to go over the output extremely carefully to make sure its correct, whereas you’d have some level of trust with a human employee.

AI certainly has a place in a technical workflow, but it’s nowhere close to replacing human workers, at least not right now. It’ll keep eating at the fringes for the next 5 years minimum, if not indefinitely, and I think the net result will be making human workers more productive, not replacing human workers. And the more productive we are per person, the more valuable that person is, and the more work gets generated.

We are 18 months into AI replacing me in 6 months. I mean… the CEO of OpenAI as well as many researchers have already said LLMs have mostly reached their limit. They are “generalizers” and if you ask them to do anything new they hallucinate quite frequently. Trying to get AI to replace developers when it hasn’t even replaced other menial office jobs is like saying “we taught AI to drive, it will replace all F1 drivers in 6 months”.

It’s tons easier to repkace CEOs, HR, managers and so on than coders. Coders needs to be creative, an HR or manager not so much. Are they leaving three months from now you think?

I’ll start worrying when they are all gone.

The real work of software engineering isn’t the coding. That is like saying that being a doctor is all about reading health charts. Planning, designing, testing and maintaining software is the hard part, and it is often much more political than it is a technical challenge. I’m not worried about getting replaced by AI. In fact, LLMs ability to generate high volumes of code only makes the skills to understand it to be more in demand.

An inherent flaw in transformer architecture (what all LLMs use under the hood) is the quadratic memory cost to context. The model needs 4 times as much memory to remember its last 1000 output tokens as it needed to remember the last 500. When coding anything complex, the amount of code one has to consider quickly grows beyond these limits. At least, if you want it to work.

This is a fundamental flaw with transformer - based LLMs, an inherent limit on the complexity of task they can ‘understand’. It isn’t feasible to just keep throwing memory at the problem, a fundamental change in the underlying model structure is required. This is a subject of intense research, but nothing has emerged yet.

Transformers themselves were old hat and well studied long before these models broke into the mainstream with DallE and ChatGPT.

There will still be people who are able to rapidly learn with AI on their side and people who fail to do it. The fast ones are able to focus much more on the underlying problems and less on language specifics. The definition of low level and high level programers will change in the context of AI. Nobody today is implementing a linked list outside of university and in the future nobody will be needed to write the repeated code which still is needed in a lot of frameworks.

But of course there might the point of a critical collapse if AI only learns from its own code and inefficient code gets repeated constantly.

GitHub Copilot does chat like ChatGPT, and writes code based on a prompt. I have decades of experience programming and I use it a lot. It gives me starting points, boilerplate and examples. It won’t build a whole application with no coding work from the human.

ChatJippity

I’ll start using that!

if lineGoUp {

    CollectUnearnedBonus()

} else {

   FireSomePeople()
   CollectUnearnedBonus()

}