Management greed, stupidity, and self serving is perennial. Nothing new there.
Nassim Nicholas Taleb said in “The Black Swan” that he thought one of the unrecognized strengths of stock-market-based economies was that as publicly traded companies grow and get older, they tend to become bloated and incapable, and lose money and eventually die; and this represents a mechanism for redistributing wealth away from the investing classes (“the rich”) with some of the money making its way back into society as a whole.
IDK if that’s still true or ever was, but he was extremely successful working in finance; he wasn’t just some idiot saying his opinions.
Doesn’t work as well these days when everything is too big to fail and gets bailed out, instead of letting the economy endure the destruction part of creative destruction.
This is kind of like saying in war, old weapons getting phased out reduces violence. While I am relieved I don’t have to worry about musket ball injuries, the new weapons are more effective at what they are designed to do and the people driving the need for newer and more effective weapons have not fundamentally changed in their motivations.
The businesses themselves aren’t driving the economy or how the megarich really make their money. Businesses are only the tools used by what’s actually driving the economy which is Capital. The same Capitalists which drive businesses to behave ruthlessly in a marketplace, grow rapidly, and ultimately collapse under their own weight will simply reinvest in an entity which will competently bring in a return on investment. The only redistribution of wealth happening is Capital investment being diverted to other tools of Capitalism. Capitalists don’t care which businesses or industry they’re investing in, they only care about maximising the return on their investment and using their influence to ensure that happens as much as possible.
When I think of historical wealth distribution which has had major impact on the lives of regular people, I can’t think of any which were caused by an outdated business clearing up some room in the market for newer and more lucrative capital investments to take its place. I have seen it through government action though.
The investment class realized it’s way more profitable to cellar box a struggling company and that you can short sell the stock and never have to pay up when the company goes bankrupt. Free money!
Can’t wait for a story from a developer or sysadmin that knows how all the duct tape is held together, gets laid off and refuses to come back to fix everything. Then the former employer doubles doubt and threatens to sue them for loss of revenue. It would be absurd but I expect the absurd now.
Pah! I can fail at my job much faster than any goshdarn AI!
Another idiot writer missing how AI works… along with every other automation and productivity increase.
I literally automate jobs for a living.
My job isn’t to eliminate the role of every staff member in a department, it’s to take the headcount from 40 to 20 while having the remaining 20 be able to produce the same results. I’ve successfully done this dozens of times in my careers, and generative AI is now just another tool we can use to get that number down a little bit lower or more easily than we could before.
Will I be able to take a unit of 2 people down to 0 people? No, I’ve never seen a process where I could eliminate every human.
Cory Doctorow is an idiot writer? Do you know of him and you’ve reached this conclusion, or you don’t know who he is and just throwing shade?
I am curious. How much follow-up do you do after your automations 1 year later to see how the profit and loss picture of the department has worked out after your work is done?
(Not that that’s the point; I think you’ll get very little sympathy here for “I help the already-rich to keep more of the productive output of the world and make sure workers keep less” even if you can make an argument that you can do it effectively.)
I’ve been following Doctorow for decades now (BoingBoing) and yes, he’s an idiot in this situation.
I’m still working with the organizations I started automating for more than a decade ago. I’m sitting in the office of one of them right now. It’s worked out great, nobody is complaining about the fact that this office space now has people at separated desks instead of crunched together like they were when I started. If it makes you feel any better, I almost exclusively do this for government and public organizations (I’m at a post-secondary education institution right now) though I really don’t care.
Stopping or stalling productivity improvements is stupid, that job is effectively useless if it can be automated, it’s nothing more than make-work to keep it. We should pass laws to redistribute wealth to solve that problem, not keep them in useless jobs by preventing automation.
You’re still working simultaneously with dozens of different organizations? Maybe I’m misunderstanding something.
Stopping or stalling productivity improvements is stupid, that job is effectively useless if it can be automated, it’s nothing more than make-work to keep it. We should pass laws to redistribute wealth to solve that problem, not keep them in useless jobs by preventing automation.
Like a lot of things, the devil is in the details. Almost everyone’s firsthand experience with consultants coming in and enacting “efficiency” is that it’s bad for both the employees obviously, but also bad for the business. I’m not saying that’s the impact of what you’re doing, just what most people’s experience is going to be.
So there’s a central question in AI: Once the machines can do everything for us, does that mean everyone eats for free? Or no one eats? What would your answer to that question be?
I sat in a room of probably 400 engineers last spring and they all laughed and jeered when the presenter asked if AI could replace them. With the right framework and dataset, ML almost certainly could replace about 2/3 of the people there; I know the work they do (I’m one of them) and the bulk of my time is spent recreating documentation using 2-3 computer programs to facilitate calculations and looking up and applying manufacturer’s data to the situation. Mine is an industry of high repeatability and the human judgement part is, at most, 10% of the job.
Here’s the real problem. The people who will be fully automatable are those with less than 10 years experience. They’re the ones doing the day to day layout and design, and their work is monitored, guided, and checked by an experienced senior engineer to catch their mistakes. Replacing all of those people with AI will save a ton of money, right up until all of the senior engineers retire. In a system which maximizes corporate/partner profit, that will come at the expense of training the future senior engineers until, at some point, there won’t be any (/enough), and yet there will still be a substantial fraction of oversight that will be needed. Unfortunately, ML is based on human learning and replacing the “learning” stage of human practitioner with machines is going to eventually create a gap in qualified human oversight. That may not matter too much for marketing art departments, but for structural engineers it’s going to result in a safety or reliability issue for society as a whole. And since failures in my profession only occur in marginal situations (high loads - wind, snow, rain, mass gatherings) my suspicion is that it will be decades before we really find out that we’ve been whistling through the graveyard.
Yeah. This is something that to me isn’t getting enough attention in the whole conversation. I’m trying to get myself up to speed on how to code effectively with AI tools, but I feel like understanding the code at a deep level is required in order to be able to do that effectively.
In the future, I think the “earning” that gives you that type of knowledge won’t be something that people are forced to go through anymore, because AI can do the simple stuff for them, and so the inevitable result is that very few people will be able to do more than rely on the AI tools to either get it right or not, because they don’t understand the underlying systems. I’m honestly not sure what future is in store a couple generations from now other than most people being forced to trust the AI (whatever its capabilities or incapabilities are at that point). That doesn’t sound like a good scenario.
The future is already here. This will sound like some old man yelling at clouds, but the tools available for advanced structural design (automatic environmental loading, finite element modeling) are used by young engineers as magical black boxes which spit out answers. That’s little different than 30 years ago when the generation before me would complain that calculators, unlike sliderules, were so disconnected from the problem that you could put in two numbers, hit the wrong operation, and get a non-sensical answer but believe it to be correct because the calculator told you so.
This evolution is no different, it’s just that the process of design (wither programming or structures or medical evaluation) will be further along before someone realizes that everything that’s being offered is utter shit. I’m actually excited about the prospect of AI/ML, but it still needs to be handled like a tool. Modern machinery can do amazing things faster, and with higher precision, than hand tools - but when things go sideways they can also destroy things much quicker and with far greater damage.
that will come at the expense of training the future senior engineers until, at some point, there won’t be any (/enough)
Anything a human can be trained to do, a neural network can be trained to do.
Yes, there will be a lack of trained humans for those positions… but spinning up enough “senior engineers” will be as easy as moving a slider on a cloud computing interface… or remote API… done by whichever NN comes to replace the people from HR.
ML is based on human learning and replacing the “learning” stage of human practitioner with machines is going to eventually create a gap in qualified human oversight
Cue in the humanoid robots.
Better yet: outsource the creation of “qualified oversight”, and just download/subscribe to some when needed.
Anything a human can be trained to do, a neural network can be trained to do.
Citation needed
Anything a human can be trained to do, a neural network can be trained to do.
Come on. This is a gross exaggeration. Neural nets are incredibly limited. Try getting them to even open a door. If we someday come up with a true general AI that really can do what you say, it will be as similar to today’s neural nets as a space shuttle is to a paper airoplane.
I’m assuming you’re being facetious. If not…well, you’re on the cutting edge of MBA learning.
There are still some things that just don’t get into books, or drawings, or written content. It’s one of the drawbacks humans have - we keep some things out our brains that just never make it to paper. I say this as someone who has encountered conditions in the field that have no literature on the effect. In the niches and corners of any practical field there are just a few people who do certain types of work, and some of them never write down their experiences. It’s frustrating as a human doing the work, but it would not necessarily be so to a ML assistant unless there is a new ability to understand and identify where solutions don’t exist and go perform expansive research to extend the knowledge. More importantly, it needs the operators holding the purse to approve that expenditure, trusting that the ML output is correct and not asking it to extrapolate in lieu of testing. Will AI/ML be there in 20 years to pick up the slack and put it’s digital foot down stubbornly and point out that lives are at risk? Even as a proponent of ML/AI, I’m not convinced that kind of output is likley - or even desired by the owners and users of the technology.
I think AI/ML can reduce errors and save lives. I also think it is limited in the scope of risk assessment where there are no documented conditions on which to extrapolate failure mechanisms. Heck, humans are bad at that, too - but maybe more cautious/less confident and aware of such caution/confidence. At least for the foreseeable future.
He has literal examples of head count increasing due to this use of ai, he’s not the idiot here.
Anecdote are not statistics.
Head counts increasing at one company are often offset by losses from their competitors as they take market share due to increased productivity.
The number of auto mechanics went up as the number of horse ranchers went down.
As someone who works for a very large company, on a team with around 500 people around the world, this is what concerns me. Our team will not be 500 people in a few years, and if it is, it’s because usage of our product has grown substantially. We are buying heavily into AI, and yet people are buying it when our leadership teams claim it will not impact jobs.
Will I be able to take a unit of 2 people down to 0 people? No, I’ve never seen a process where I could eliminate every human.
Socially speaking, this is also very concerning to me. I’m afraid that implementation of AI will be yet another thing that makes it difficult for smaller businesses to compete in a global marketplace. Yes, a tech-minded company can leverage a smaller head count into more capabilities, but this typically requires more expensive and limiting turnkey solutions, or major investment into developers of a customized solution.
I honestly have no idea what the solution is. To me the issue is that with technology where it is, only about 20% of us actually have to do any work to keep all the wheels turning and provide for everyone. So far, in the western world, the solution has been to occupy people with increasingly-bullshit jobs (and, for some reason, not giving a lot of people who do the actual work enough to live on), but as technology keeps getting more and more powerful we’re more and more being faced with the limits of “you have to work to live” as a way to set things up.
The solution to both bullshit jobs and no life could have been to downscale work time not amount of people. If 20% of people is enough to do the job, maybe it’s better to keep everyone but let them work only 20% of time?
That won’t pass the shareholders’ vote, of course, because optimization must only mean “money optimization”
