How stupid do you have to be to believe that only 8% of companies have seen failed AI projects? We can’t manage this consistently with CRUD apps and people think that this number isn’t laughable? Some companies have seen benefits during the LLM craze, but not 92% of them. 34% of companies report that generative AI specifically has been assisting with strategic decision making? What the actual fuck are you talking about?
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I don’t believe you. No one with a brain believes you, and if your board believes what you just wrote on the survey then they should fire you.
This. Many of these tools are good at incredibly basic boilerplate that’s just a hint outside of say a wizard. But to hear some of these AI grifters talk, this stuff is going to render programmers obsolete.
There’s a reality to these tools. That reality is they’re helpful at times, but they are hardly transformative at the levels the grifters go on about.
Copilot / LLM code completion feels like having a somewhat intelligent helper who can think faster than I can, however they have no understanding of how to actually code, but are good at mimicry.
So it’s helpful for saving time typing some stuff, and sometimes the absolutely weird suggestions make me think of other scenarios I should consider, but it’s not going to do the job itself.
Looks like two people suckered by the grifters downvoted your comment (as of this writing). Should they read this, it is a grift, get over it.
I interviewed a candidate for a senior role, and they asked if they could use AI tools. I told them to use whatever they normally would, I only care that they get a working answer and that they can explain the code to me.
The problem was fairly basic, something like randomly generate two points and find the distance between them, and we had given them the details (e.g. distance is a straight line). They used AI, which went well until it generated the Manhattan distance instead of the Pythagorean theorem. They didn’t correct it, so we pointed it out and gave them the equation (totally fine, most people forget it under pressure). Anyway, they refactored the code and used AI again to make the same mistake, didn’t catch it, and we ended up pointing it out again.
Anyway, at the end of the challenge, we asked them how confident they felt about the code and what they’d need to do to feel more confident (nudge toward unit testing). They said their code was 100% correct and they’d be ready to ship it.
They didn’t pass the interview.
And that’s generally my opinion about AI in general, it’s probably making you stupider.
I use them like wikipedia: it’s a good starting point and that’s it (and this comparison is a disservice to wikipedia).
Yes, and then you take the time to dig a little deeper and use something agent based like aider or crewai or autogen. It is amazing how many people are stuck in the mindset of “if the simplest tools from over a year aren’t very good, then there’s no way there are any good tools now.”
It’s like seeing the original Planet of the Apes and then arguing against how realistic the Apes are in the new movies without ever seeing them. Sure, you can convince people who really want unrealistic Apes to be the reality, and people who only saw the original, but you’ll do nothing for anyone who actually saw the new movies.
I’ve used crewai and autogen in production… And I still agree with the person you’re replying to.
The 2 main problems with agentic approaches I’ve discovered this far:
One mistake or hallucination will propagate to the rest of the agentic task. I’ve even tried adding a QA agent for this purpose but what ends up happening is those agents aren’t reliable and also leads to the main issue:
It’s very expensive to run and rerun agents at scale. The scaling factor of each agent being able to call another agent means that you can end up with an exponentially growing number of calls. My colleague at one point ran a job that cost $15 for what could have been a simple task.
One last consideration: the current LLM providers are very aware of these issues or they wouldn’t be as concerned with finding “clean” data to scrape from the web vs using agents to train agents.
If you’re using crewai btw, be aware there is some builtin telemetry with the library. I have a wrapper to remove that telemetry if you’re interested in the code.
Personally, I’m kinda done with LLMs for now and have moved back to my original machine learning pursuits in bioinformatics.
Also, a lot of people who are using AI have become quiet about it of late exactly because of reactions like this article’s. Okay, you’ll “piledrive” me if I mention AI? So I won’t mention AI. I’ll just carry on using it to make whatever I’m making without telling you.
There’s some great stuff out there, but of course people aren’t going to hear about it broadly if every time it gets mentioned it gets “piledriven.”