I came across this article in another Lemmy community that dislikes AI. I’m reposting instead of cross posting so that we could have a conversation about how “work” might be changing with advancements in technology.
The headline is clickbaity because Altman was referring to how farmers who lived decades ago might perceive that the work “you and I do today” (including Altman himself), doesn’t look like work.
The fact is that most of us work far abstracted from human survival by many levels. Very few of us are farming, building shelters, protecting our families from wildlife, or doing the back breaking labor jobs that humans were forced to do generations ago.
In my first job, which was IT support, the concept was not lost on me that all day long I pushed buttons to make computers beep in more friendly ways. There was no physical result to see, no produce to harvest, no pile of wood being transitioned from a natural to a chopped state, nothing tangible to step back and enjoy at the end of the day.
Bankers, fashion designers, artists, video game testers, software developers and countless other professions experience something quite similar. Yet, all of these jobs do in some way add value to the human experience.
As humanity’s core needs have been met with technology requiring fewer human inputs, our focus has been able to shift to creating value in less tangible, but perhaps not less meaningful ways. This has created a more dynamic and rich life experience than any of those previous farming generations could have imagined. So while it doesn’t seem like the work those farmers were accustomed to, humanity has been able to shift its attention to other types of work for the benefit of many.
I postulate that AI - as we know it now - is merely another technological tool that will allow new layers of abstraction. At one time bookkeepers had to write in books, now software automatically encodes accounting transactions as they’re made. At one time software developers might spend days setting up the framework of a new project, and now an LLM can do the bulk of the work in minutes.
These days we have fewer bookkeepers - most companies don’t need armies of clerks anymore. But now we have more data analysts who work to understand the information and make important decisions. In the future we may need fewer software coders, and in turn, there will be many more software projects that seek to solve new problems in new ways.
How do I know this? I think history shows us that innovations in technology always bring new problems to be solved. There is an endless reservoir of challenges to be worked on that previous generations didn’t have time to think about. We are going to free minds from tasks that can be automated, and many of those minds will move on to the next level of abstraction.
At the end of the day, I suspect we humans are biologically wired with a deep desire to output rewarding and meaningful work, and much of the results of our abstracted work is hard to see and touch. Perhaps this is why I enjoy mowing my lawn so much, no matter how advanced robotic lawn mowing machines become.
I have been working with computers, and networks, and the internet since the 1980s. Over this span of 40-ish years, “how I work” has evolved dramatically through changes in how computers work and more dramatically through changes in information availability. In 1988 if you wanted to program an RS-232 port to send and receive data, you read books. You physically traveled to libraries, or bookstores - maybe you might mail order one, but that was even slower. Compared to today the relative costs to gain the knowledge to be able to perform the task were enormous, in time invested, money spent, and physical resources (paper, gasoline, vehicle operating costs).
By 20 years ago, the internet had reformulated that equation tremendously. Near instant access to worldwide data, organized enough to be easier to access than a traditional library or bookstore, and you never needed to leave your chair to get it. There was still the investment of reading and understanding the material, and a not insignificant cost of finding the relevant material through search, but the process was accelerated from days or more to hours or less, depending on the nature of the learning task.
A year ago, AI hallucination rates made them curious toys for me - too unreliable to be of net practical value. Today, in the field of computer programming, the hallucination rate has dropped to a very interesting point: almost the same as working with a not-so-great but still useful human colleague. The difference being: where a human colleague might take 40 hours to perform a given task (not that the colleague is slow, just it’s a 40 hour task for an average human worker), the AI can turn around the same programming task in 2 hours or less.
Humans make mistakes, they get off on their own tracks and waste time following dead ends. This is why we have meetings. Not that meetings are the answer to everything, but at least they keep us somewhat aware of what other members of the team are doing. That not so great programmer working on a 40 hour task is much more likely to create a valuable product if you check in with them every day or so, see “how’s it going”, help them clarify points of confusion, check their understanding and direction of work completed so far. That’s 4 check points of 15 minutes to an hour in the middle of the 40 hour process. My newest AI colleagues are ripping through those 40 hour tasks in 2 hours, impressive, and when I don’t put in the additional 2 hours of managing them through the process, they get off the rails, wrapped around the axles, unable to finish a perfectly reasonable task because their limited context windows don’t keep all the important points in focus throughout the process. A bigger difficulty is that I don’t get 23 hours of “offline wetware processing” between touch points to refine my own understanding of the problems and desired outcomes.
Humans have developed software development processes to help manage human shortcomings, humans’ limited attention spans and memory. We still out-perform AI in some of this context window span thing, but we have our own non-zero hallucination rates. Asking an AI chatbot to write a program one conversational prompt at a time only gets me so far. Providing an AI with a more mature software development process to follow gets much farther. AI isn’t following these processes (that it helped to translate from human concepts into its own language of workflows, skills, etc.) 100% perfectly, I catch it skipping steps in simple 5 step workflows, but like human procedures, there’s a closed loop procedure improvement procedure to help perform better in the future.
Perhaps most importantly, the procedures are constantly reminding AI to be “self aware” of its context window limitations, do RAG (research augmented generation) of best practices for context management, DRY (reduce through non-repetition and use of references to single points of truth) its own procedures and documentation it generates. Will I succeed in having AI rebuild a 6 month project I did five years back, doing it better this time - expanding its scope to what would have been a year long development effort if I had continued doing it solo? Unclear, I’m two weeks in and I feel like I’m about where I was after two weeks of development last time, but it also feels like I have a better foundation to complete the bigger scope this time using the AI tools, and there’s that tantalizing possibility that at any point now it might just take off and finish it by itself.