Ok, so this blog is for my musings, and this is very much a musing. However, a couple recent things have prompted some thoughts. The issue is Large Language Models (LLMs). As I’ve said, I have no problem with the tech inherently. It’s really optimized for language roles (as the name implies). What is concerning to me is the hype, and so the use. And, it’s led me to wonder if it is, or what is, the right tech for the job.
So, up until LLMs, when you wanted something done, you built the appropriate tech. You put together specifications, and development teams built it. (And then they asked UI to fix the problems, as Don Norman talked about in The Invisible Computer. Probably then handed off to training folks to address the problems from the bad design outside the UI.). It took time, and money. And, if you didn’t use something like Watts Humphrey’s Personal & Team Software Processes, you likely took too long, and had too many errors.
You could use AI for more decision tasks. So, either symbolic if well-defined, or based upon training data and machine learning if ill-defined in principle. You, of course, still have to live with or address the biases in the rules or databases. And, of course, the brittleness at the edges of the decision space. Still, all told, we had approaches. Then, the world changed.
So, for one, my colleague Kevin Wheeler (a deep expert in the talent world), talked about how the tasks of the talent function are in flux. What he cited was that many of the rote tasks were being made redundant. Which is good, I opined, if we’re removing tasks that people aren’t good at (I’ve said before that we should be doing pattern-matching and meaning-making, and leaving rote to the machines). However, there’s the problem of developing the expertise. So, for instance, as Etienne Wenger and Jean Lave talk about in communities of practice, moving from peripheral tasks to central as you understand the domain, But you need those peripheral tasks!
Plus, we’re seeing people being laid off. Meta just announced another 8000 being laid off, for efficiencies, and there have been announcements from many of the big orgs (and small ones are making similar decisions). Cutting through the smoke, what we sees is that folks face increasing expectations to use AI to do things faster, and the expectations are increasing (without, mind you, an increase in rewards for same). But, increasingly we’re not working together as we used to.
What also showed up in a LinkedIn conversation is the expectation that we can ask LLMs to do the things that we used to do by writing software. And yes, such systems make mistakes, but so do humans, right? Yes, and…we can assign responsibilities with humans, and they’ll be corruptible and more, but we have compliance to deal with that. As Markus Bernhardt is pointing out, we’re not doing a good job on that with our systems. We, too often, haven’t worried about the necessary guardrails, and security, and responsibility, and governance, and….
What is concerning me, as folks like Mark Britz talk about, is that we’re losing the human connection. We’re losing:
- the upward path for new folks
- the continual development of our own capability
- the necessary checks and balances that keep our systems secure
Really, we’re turning away from doing things together to maximize outcomes, and instead are working to be more expedient. In short, we’re trading off effectiveness to achieve efficiency. And ignoring ethics along the way.
In short, I fear we’re using the ease of doing things with LLMs to avoid the hard work of doing things the right way. Which I resist. I am seeing quite the backlash amongst the folks forced to use AI. Which includes the pressures by execs to use it, and the eagerness of those to promote it who stand to gain (whether vendors or consultants). We see research results showing that folks are thinking less, execs have unrealistic beliefs about how productive it makes people, and it’s pretty simple to recognize that the costs can’t stay as they are. The illusions from smoke and mirrors aren’t a good basis on which to plan.
Don’t get me wrong, I do see the benefits of LLMs. I just see them in context of the bigger picture of people, tech, and the broader picture of AI. And I could be wrong, it’s true. It’s just that there’re some reasons to believe that decades of immersion in the relevant fields have some basis for questioning whether we’re using the right tech for the job. I welcome your thoughts!
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