As always, I use the term “AI” loosely. I’m referring to these scary LLMs coming for our jobs.
It’s important to state that I find LLMs to be helpful in very specific use cases, but overall, this is clearly a bubble, and the promises of advance have not appeared despite hundreds of billions of VC thrown at the industry.
So as not to go full-on polemic, we’ll skip the knock-on effects in terms of power-grid and water stresses.
No, what I want to talk about is the idea of software in its current form needing to be as competent as the user.
Simply put: How many of your coworkers have been right 100% of the time over the course of your career? If N>0, say “Hi” to Jesus for me.
I started working in high school, as most of us do, and a 60% success rate was considered fine. At the professional level, I’ve seen even lower with tenure, given how much things turn to internal politics past a certain level.
So what these companies are offering is not parity with senior staff (Ph.D.-level, my ass), but rather the new blood who hasn’t had that one fuckup that doesn’t leave their mind for weeks.
That crucible is important.
These tools are meant to replace inexperience with incompetence, and the beancounters at some clients are likely satisfied those words look similar enough to pass muster.
We are, after all, at this point, the “good enough” country. LLM marketing is on brand.
Depends on what job it’s replacing. LLMs are so-called narrow intelligence. They’re built to generate natural-sounding language, so if that’s what the job requires, then even an imperfect LLM might be fit for it. But if the job demands logic, reasoning, and grounding in facts, then it’s the wrong tool. If it were an imperfect AGI that can also talk, maybe - but it’s not.
My unpopular opinion is that LLMs are actually too good. We just wanted something that talks, but by training it on tons of correct information, they also end up answering questions correctly as a by-product. That’s neat, but it happens “by accident” - not because they actually know anything.
It’s kind of like a humanoid robot that looks too much like a person - we struggle to tell the difference. We forget what it really is because of what it seems.