Prior hype, like block chain are more abstract, therefore less useful to people who understand managing but not the actual work.
Because a core feature of LLMs is to minimize the distance between {quality answers} and {gibberish that looks correct}.
As a consequence, this maximizes {skill required to distinguish the two}.
Are we then surprised that non-subject matter experts overestimate the output's median usefulness?
In fact, we should try to LLM them away. I wonder, would LLMs then be promoted less?
Actually, I feel like executing this startup and pitching would be hilarious and therapeutic.
"How we will eliminate your job with LLMs, MBA."
To manage this well, you need the courage to trust people, as well as the intelligence and patience to question them. Not everybody has that.
But that aside, I think business people generally like having (what they think are) strong experts. It means they can use their people skills and networks to create competitive advantage.
The "copilot experiences", that finishes the next few lines can be useful and intuitive - an "agent" writing anything more than boilerplate is bound to create more work than it lifted in my experience.
Where I am having a blast with LLMs is learning new programming languages more deeply. I am trying to understand Rust better - and LLMs can produce nice reasoning to whether one should use "Vec<impl XYZ>" or "Vec<Box<dyn XYZ>>". I am sure this trivial for any experienced Rust developer though.
I feel it degrades a whole group of people to a specific stereotype that might or might not be true.
How about lawyers, PhDs, political science majors, etc.
Let’s look at the humans and their character, not titles.
By the way, I have an MBA too and feel completely misjudged with statements like that.
An analogue to this would be "all cops are bastards". Sure, there are some good ones out there, but there are enough bad ones out there that the stereotype generally applies. The statement is a rallying cry for something to be done about it. The "guilty by association" bit that tends to follow is another thing entirely.
> Is because unlike prior hype cycles, this one is super easy for an MBA to point at and sort of see a way to integrate it.
This particular hype is the easiest one thus far for an MBA to understand because employing it is the closest thing to a Ford assembly line[0] the software industry has made available yet.
Since the majority of management training centers on early 20th century manufacturing concepts, people taught same believe "increasing production output" is a resource problem, not an understanding problem. Hence the allure of "generative AI can cut delivery times without increasing labor costs."
