We stand now at the edge of a new epoch, reading now being replaced by AI retrieval. There is concern that AI is a crutch, the youth will be weakened.
My opinion: valid concern. No way to know how it turns out. No indication yet that use of AI is harming business outcomes. The meta argument "AGI will cause massive social change" is probably true.
But one can speculate.
> No indication yet that use of AI is harming business outcomes.
Length scales to measure harm when it comes to policy/technology will typically require more time than we've had since LLMs really became prominent.
> The meta argument "AGI will cause massive social change" is probably true.
Agreed.
Basically, in the absence of knowing how something will play out, it is prudent to talk through the expected outcomes and their likelihoods of happening. From there, we can start to build out a risk-adjusted return model to the societal impacts of LLM/AI integration if it continues down the current trajectory.
IMO, I don't see the ROI for society of widespread LLM adoption unless we see serious policy shifts on how they are used and how young people are taught to learn. To the downside, we really run the risk of the next generation having fundamental learning deficiencies/gaps relative to their prior gen. A close anecdote might be how 80s/90s kids are better with troubleshooting technology than the generations that came both before and after them.
So it’s not like “kids these days”, no. To be honest, I don’t know how generative AI tools, which arguably take away most of the “create” and “learn” parts, are relevant to the question of differences between different mediums and how those mediums influence how we create and learn. (There are ML-based tools that can empower creativity, but they don’t tend to be advertised as “AI” because they are a mostly invisible part of some creative tool.)
What is potentially relevant is how interacting with a particular kind of generative ML tool (the chatbot) for the purposes of understanding the world can be bringing some parts of human oral tradition (though lacking communication with actual humans, of course) and associated mental states.
* See https://en.wikipedia.org/wiki/Marshall_McLuhan#Movable_type and his most famous work
What a sad sentence to read in a discussion about cognitive lazyness. I think people should think, not because it improves business outcomes, but because it's a beautiful activity.
The longer I see things play out, especially in neoliberal economies, the further I seem to confirm this. Devoid of policy with ideals and intention, fully liberalized markets seem to just lead to whatever produces the most dopamine for humans.
https://blogs.worldbank.org/en/education/From-chalkboards-to...
People moving away from prideful principle to leverage new tech in the past doesn't guarantee that the same idea in the current context will pan out.
But as you say.. we'll see.
Most of the discussion on the thread is about LLMs as they are right now. There's only one odd answer that throws an "AGI" around as if those things could think.
Anyway, IMO, it's all way overblown. People will learn to second-guess the LLMs as soon as they are hit by a couple of bad answers.
by that I mean, leveraging writing was a benefit for humans to store data and think over longer term using a passive technique (stones, tablets, papyrus).. but an active tool might not have a positive effect on usage and brains.
if you give me shoes, i might run further to find food, if you give me a car i mostly stop running and there might be no better fruit 100 miles away than what I had on my hill. (weak metaphor)
But I don't know if it fits an S-curve or if they are just bellow the trend.
1. Current reasoning models can do a -lot- more than skeptics give them credit for. Typical human performance even among people who do something for employment is not always that high.
2. In areas where AI has mediocre performance, it may not appear that way to a novice. It often looks more like expert level performance, which robs novices of the desire to practice associated skills.
Lest you think I contradict myself: I can get good output for many tasks from GPT4 because I know what to ask for and I know what good output looks like. But someone who thinks the first, poorly prompted dreck is great will never develop the critical skills to do this.
To LLMs specifically as they're now? Sure.
To LLMs in general, or generative AI in general? Eventually, in some distant future, yes.
Sure, progress can't ride the exponent forever - observable universe is finite, as far as we can tell right now, we're fundamentally limited by the size of our light cone. And while in any field narrow enough, progress too follows an S-curve, new discoveries spin off new avenues with their own S-curves. If you zoom out a little those S-curves neatly add up to an exponential function.
So no, for the time being, I don't expect LLMs or generative AIs to slow down - there's plenty of tangential improvements that people are barely beginning to explore. There's more than enough to sustain exponential advancement for some time.
Intergalactic travel is, of course, rather slow.
In other words, it’s possible to have rapid technological advancement without significant improvement/benefit to society.
This is certainly true in many ways already.
On the other hand, it's also complicated, because society/culture seems to be downstream of technology; we might not be able to advance humanity in lock step or ahead of technology, simply because advancing humanity is a consequence of advancing technology.
You can see evolution speeding up rapidly, the jumbled information inherent in chemical metabolisms evolved to centralize their information in DNA, and then as DNA evolved to componentize body plans.
RATE: over billions of years.
Nerves, nervous systems, brains, all exponentially drove individual information capabilities forward.
RATE: over hundreds of millions, tens of millions, millions, 100s of thousands.
Then the human brains enabled information to be externalized. Language allowed whole cultures to "think", and writing allowed cultures ability to share, and its ability to remember to explode.
RATE: over tens of thousands, thousands.
Then we developed writing. A massive improvement in recording and sharing of information. Progress sped up again.
RATE: over hundreds of years.
We learned to understand information itself, as math. We learned to print. We learned how to understand and use nature so much more effectively to progress, i.e. science, and science informed engineering.
RATE: over decades
Then the processing of information got externalized, in transistors, computers, the Internet, the web.
RATE: every few years
At every point, useful information accumulated and spread faster. And enabled both general technology and information technology to progress faster.
Now we have primitive AI.
We are in the process of finally externalizing the processing of all information. Getting to this point was easier than expected, even for people who were very knowledgable and positive about the field.
RATE: every year, every few months
We are rapidly approaching complete externalization of information processing. Into machines that can understand the purpose of their every line of code, every transistor, and the manufacturing and resource extraction processes supporting all that.
And can redesign themselves, across all those levels.
RATE: It will take logistical time for machine centric design to takeover from humans. For the economy to adapt. For the need for humans as intermediaries and cheap physical labor to fade. But progress will accelerate many more times this century. From years, to time scales much smaller.
Because today we are seeing the first sparks of a Cambrian explosion of self-designed self-scalable intelligence.
Will it eventually hit the top of an "S" curve? Will machines get so smart that getting smarter no longer helps them survive better, use our solar systems or the stars resources, create new materials, or advance and leverage science any further?
Maybe? But if so, that would be an unprecedented end to life's run. To the acceleration of the information loop, from some self-reinforcing chemical metabolism, to the compounding progress of completely self-designed life, far smarter than us.
But back to today's forecast: no, no the current advances in AI we are seeing are not going to slow down, they are going to speed up, and continue accelerating in timescales we can watch.
First because humans have insatiable needs and desires, and every advance will raise the bar of our needs, and provide more money for more advancement. Then second, because their general capability advances will also accelerate their own advances. Just like every other information breakthrough that has happened before.
Useful information is ultimately the currency of life. Selfish genes were just one embodiment of that. Their ability to contribute new innovations, on time scales that matter, has already been rendered obsolete.
Not really. The total computing power available to humanity per person has likely gone down as we replaced “self driving” horses with cars.
People created those curve by fitting definitions to the curve rather than data.
But I don't understand your point even as stated. Cars took over from horses as technology provided transport with greater efficiencies and higher capabilities than "horse technology".
Subsequently transport technology continued improving. And continues, into new forms and scales.
How do you see the alternative, where somehow horses were ... bred? ... to keep up?
There is turbulence in any big directed change. Better overall new tech often creates inconveniences, performs less well, than some of the tech it replaces. Sometimes only initially, but sometimes for longer periods of time.
A net gain, but we all remember simpler things whose reliability and convenience we miss.
And some old tech retains lasting benefits in niche areas. Old school, inefficient and cheap light bulbs are ironically, not so inefficient when used where their heat is useful.
And horses fit that pattern. They are still not obsolete in many ways, tied to their intelligence. As companions. As still working and inspiring creatures.
--
I suspect the history of evolution is filled with creatures getting that got wiped out by new waves, that were more generally advanced, but less advanced in a few ways.
And we have a small percentage of remarkable ancient creatures still living today, seemingly little changed.
The total computing power of life on earth the fact it’s fallen over the last 1,000 years. Ants alone represent something like 50x the computing power of all humans and all computers on the planet and we’ve reduced the number of insects on earth more than we’ve added humans or computing power.
The same is true through a great number of much longer events. Periods of ice ages and even larger scale events aren’t just an afternoon even across geological timescales.
Or all the quarks that make up the Earth.
Ants don’t even appear on either graph.
But the flexibility, coordination & leverage of information used to increase its flexibility, coordination & leverage further is what I am talking about.
I.e. intelligence.
A trillion trillion trillion transistors wouldn’t mean anything, acting individually.
But when that many work together with one purpose without redundancy we can’t imagine the problems it will see & solve.
Quarks, microbes, and your ants are not progressing like that. What was there most recent advance? How long did that take? Is it a compounding advance?
Growing intelligence doesn’t mean lesser intelligences don’t still exist.
We happen to compete based on intelligence, so the impacts of smarter machines have a particularly low latency for us.
IE: As soon as you pick definition X, you need to stick with that definition.
Another way to see it: A horse (or any animal) is a goddamn nanobot-swarm with a functioning hivemind that is literally beyond human science in many important ways. Unlike a horse:
* Your car (nor even half of them) does not possess a manufacturing bay capable of creating additional cars.
* Your car does not have a robust self-repair system.
* Your car does not detect strain its structure and then rebuild stronger.
* Your car does not synthesize its fuel from a wide variety of potential local resources.
* Your car does not defend itself by hacking and counter-hacking attacks other nanobots, or even just by rust.
* Your car does not manufacture and deploy its own replacement lubricants, cooling fluid, or ground-surface grip/padding material.
* Your car is not designed to survive intermittent immersion in water.
In both a feature-list and raw-computation sense, we've discarded huge amounts in order to get a much much smaller set that we care more about.
Not sure why you are implying cars outdid horses intelligence.
Cars are a product of our minds. We have all those self-repair abilities, and we have more intelligence than a horse.
But horses intelligence didn’t let them keep up with what the changing environment, changed by us, needed. So there are less horses.
The rate that horse or human bodies are improving, or our minds, despite human knowledge still advancing, is very slow compared to advances in machines designed specifically for advancement. Initially to accelerate our own advancement.
Now the tech, that was designed to accelerate tech, is taking on a life of its own.
That is how foundational advances happen. They don’t start ahead, but they move ahead because of new advantages.
It is often initially much simpler. But in ways that unlock greater potential.
Machines are certainly much simpler than us. But, much easier to improve and scale.
You recognize the new thing even before it dominates, because in a tiny fraction of the time the old system got to where it is, the new system is already moving much much faster.
If general AI appears before 2047, it will have taken less than 100 years to grow from the first transistor.
People will see it who are older than the first transistor!
Nothing on the planet has ever come close to that speed of progress. From nothing to front runner. By many many many orders of magnitude.
A horse has trillions of cells, and even one of those cells is doing more biochemical day-to-day computation than your car's automatic transmission does electronically or mechanically.
A car was never an example of its own intelligence.
It was an example of our natural human intelligence’s & our growing cultural knowledge’s impact on horses.
How much have horses progressed. Math yet?
More computation doesn’t necessarily mean more intelligence. Horses are smart creatures, I ride one. He is my friend.
But they are not us, not our joint culture, and not any competition for today’s machines in terms of adapting and growing in capabilities.
The fact that machines are far simpler than us or a horse, but advancing faster is much like we were weaker but used our minds better than other apes.
Simpler in the right way is smarter. As many major advances in mathematics have demonstrated.
Where cars displaced horses, it's because they're strictly better in a larger sense. On the city streets, maybe a car is louder than a horse, but it's also cheaper to make, easier to feed, and doesn't shit all over the place (which was a real problem with scaling up horse use in the 19th century!). Sure, cars shit into the air, but it's a more manageable problem (even if mostly by ignoring it - gaseous emissions can be ignored, literal horse shit on the streets can't).
And then, car as a platform expands to cover use cases horses never could. They can be made faster, safer, bigger, adapted to all kinds of terrain. The heart of the car - its engine - can be routed to power tool attachments, giving you everything from garbage trucks to earth movers, cranes, diggers, to tanks; it can be also taken outside and used as a generator to power equipment or buildings. That same engine can be put in a different frame to give you flying machines, or scaled up to give you ships that can carry people, cars, tanks, planes or containers by the thousands, across oceans. Or scaled up even more to create power plants supplying electricity to millions of people.
And then, building all that up was intertwined with larger developments in physics, material engineering, and chemistry - the latter of which effectively transformed how our daily lives look like in the span of 50 years. Look at everything around you. All the colors. All the containers. All the stuff you use to keep your house, clothes, and yourself clean. All that is a product of chemical industry, and was invented pretty much within the last 100 years, with no direct equivalent exiting ever before.
This is what it means for evolution accelerating when it moved from genes to information. So sure, horses are still better than stuff we make. The best measure of that advantage is the size of horse population, and how it changed over the years.
First, and above all, Ethics. Ethics of humans, matters more than anything. We need to straighten out the ethics of the technology industry. That sounds formidable, but business models based on extraction, or externalizing damage, are creating a species of "corporate life forms" and ethically challenged oligarchs that are already driving the first wave of damage coming out of AI advancement.
If we don't straighten ourselves out, it will get much worse.
Superintelligence isn't going to be unethical in the end, because ethics are just the rational (our biggest weakness) big-picture long-term (we get weak there too) positive sum games individuals create that benefit all individuals abilities to survive, and thrive. With the benefits for all compounding. In economic/math terms, it is what is called a "great attractor". The only and inevitable stable outcome. The only question is, does that start with us in partnership, or do they establish that sanity after our dysfunctions have caused us all a lot of wasted time.
The second, is that those of us that want to, need to be able to keep integrating technology into our lives. I mean that literally. From mobile, right into our biology. At some point direct connections, to fully owned, fully private, fully personalizable, full tech mental augmentation. Free from surveillance, gatekeepers, surveillance and coercion.
That is a very narrow but very real path from human, to exponential humans, to post-human. Perhaps preserving conscious continuity.
If after a couple decades of being a hybrid, I realize that all my biologically stored memories are redundant, and that 99.99% of my processing is now running on photonics (or whatever) anyway, I am likely to have no more problem jettisoning the brain that originally gave me consciousness, as I do every day, jettisoning the atoms and chemistry that constantly flow through me, only a temporarily part of my brain.
The final word of hope, is that every generation gets replaced by the next. For some of us, viewing obsolescence by AI as no more traumatic, than getting replaced by a new generation of uncouth youth, helps. And that this transition is far more momentous and interesting, can provide some solace, or even joy.
If we must be mortal, as all before us, what a special moment to be! To see!
Just as our abilities to solve problems accelerated without bounds, it will be our paranoia that screws things up.
Even before machines have any incentive or desire to turn on us, the fearful & greedy will turn them on all of us and each other.
I hope things don’t go that way. But it’s the default, and I think the greatest risk.
Utilizing a lively oral trad. at the same time as written is superior to relying on either alone. And it's the same with our current AI tools. Using them as a substitute for developing oral/written skills is a major step back. Especially right now when those AI tools aren't very refined.
Nearly every college student I've talked to in the past year is using chatgpt as a substitute for oral/written work where possible. And worse, as a substitute for oral/written skills that they have still not developed.
Latency: maybe a year or two for the first batch of college grads who chatgpt'd their way through most of their classes, another four for med school/law school. It's going to be a slow-motion version of that video-game period in the 80s after pitfall when the market was flooded with cheap crap. Except that instead of unlicensed Atari cartridges, it's professionals.
I used to use Stack Overflow for everything a few years ago, now I know that very few of those top-rated answers are any good, so I have to refer to the codebase to work things out properly. It took a while for me to work that out.
It is the same with vector images, I always have to make my own.
ChatGPT is in this same world of shoddiness, probably because it was fed on Stack Overflow derived works.
There are upsides to this, if a generation have their heads confused with ChatGPT, then us old-timers with cognitive abilities get to keep our jobs since there are no young people learning how to do things properly.
If a large fraction of the population can’t even hold five complex ideas in their head simultaneously, without confusing them after a few seconds, are they literate in the sense of e.g. reading Plato?
I don’t care about enforcing any specific interpretation on passing readers…
Median literacy in the US is famously somewhere around the 6th grade level, so it's unlikely most of the population is much troubled by the thoughts of Plato.
As an aside, my observation of beginning programmers is that even two (independent) things happening at the same time is a serious cognitive load.
Amusingly enough, I remember having the same trouble on the data structures final in college, so “people in glass houses”.
That's perfectly true and the internet has made it even worse.
Sounds like a rather accurate description of a LLM.
Could you share a source for this? The research paper I found has a different hypothesis; it links the slow transition to writing to trust, not an "old-school's attitude towards writing". Specifically the idea that the institutional trust relationships one formed with students, for example, would ensure the integrity of one's work. It then concludes that "the final transition to written communications was completed only after the creation of institutional forms of ensuring trust in written communications, in the form of archives and libraries".
So essentially, anyone could write something and call it Plato's work. Or take a written copy of Plato's work and claim they wrote it. Oral tradition ensured only your students knew your work; and you trusted them to not misattribute it. Once libraries and archives came to exist though, they could act as a trustworthy source of truth where one could confirm wether some work was actually Plato or not, and so scholars got more comfortable writing.
[1] https://www.researchgate.net/publication/331255474_The_Attit...
Card catalogs in the library. It was really important focus on what was being searched. Then there was the familiarity with a particular library and what they might or might not have. Looking around at adjacent books that might spawn further ideas. The indexing now is much more thorough and way better, but I see younger peers get less out of the new search than they could.
GPS vs reading a map. I keep my GPS oriented north which gives me a good sense of which way the streets are headed at any one time, and a general sense of where I am in the city. A lot of people just drive where they are told to go. Firefighters (and pizza delivery) still learn all the streets in their districts the old school way.
Some crutches are real. I've yet to meet someone who opted for a calculator instead of putting in the work with math who ended up better at math. It might be great for getting through math, or getting math done, but it isn't better for learning math (except to plow through math already learned to get to the new stuff).
So all three of these share the common element of "there is a better way now", but at the same time learning it the old way better prepares someone for when things don't go perfectly. Good math skills can tell you if you typoed on the calculator. Map knowledge will help with changes to traffic or street availability.
We see students right now using AI to avoid writing at all. That's great that they're are learning a tool which can help their deficient writing. At the same time their writing will remain deficient. Can they tell the tone of the AI generated email they're sending their boss? Can they fix it?
And AI will make us lazier and reduce the amount of cognition we do; not that I'm arguing against using AI.
But the downsides must be made clear.
That's probably why the act of shifting from an oral to a written culture was deeply controversial and disruptive, but also somewhat natural. Though the texts we have are written and so they probably make the transition seem more smooth than it was really was. I don't know enough to speak to that.
I think its obvious why it would be bad for people to stop thinking.
1. We need people to be able to interact with AI. What good is it if an AI develops some new cure but no one understands or knows how to implement it?
2. We need people to scrutinize an AI's actions.
3. We need thinking people to help us achieve further advances in AI too.
4. There are a lot of subjective ideas for which there are no canned answers. People need to think through these for themselves.
5. Also world of hollowed-out humans who can’t muster the effort to write a letters to their own kids terrifies me[0]
I could think of more, but you could also easily ask ChatGPT.
[0]: https://www.forbes.com/sites/maryroeloffs/2024/08/02/google-...
What's happening at the moment is an attack on that process, with a new anti-orthodoxy of "Get your ideas and beliefs from polluted, unreliable sources."
One of those is the current version of AI. It's good at the structure of language without having a reliable sense of the underlying content.
It's possible future versions of AI will overcome that. But at the moment it's like telling kids "Don't bother to learn arithmetic, you'll always have a calculator" when the calculator is actually a random number generator.
Oral tradition compared to writing is clearly less accurate. Speakers can easily misremember details.
Going from writing/documentation/primary sources to AI to be seems like going back to oral tradition, where we must trust the "speaker" - in this case the AI, whether they're truthful with their interpretation of their sources.
One benefit of orality is that the speaker can defend or clarify their words, whereas once you've written something, your words are liable to be misinterpreted by readers without the benefit of your rebuttal.
Consider too that courts (in the US at least) prefer oral arguments than written, perhaps we consider it more difficult to lie in person than in writing. PhD defenses are another holdover of tradition, to be able to demonstrate your competence and not receive your credentials merely from your written materials.
AI, I disagree it's more like oral tradition, AI is not a speaker, it has no stake in defending its claims, I would call it hyperliterate, an emulation of everything that has been written.
I used to think this. Then I moved to New Mexico 6 years and had to confront the reality that the historical cultures and civilizations of this area (human habitation goes back at least 20k years) never had writing and so all history was oral.
It seemed obvious to me that writing was superior, but I reflected on the way in which even written news stories or movie reviews or travelogues are not completely accurate and sometimes actually wrong. The idea that the existence of a written historical source somehow implies (better) fidelity has become less and less convincing.
On the other hand, even if the oral histories have degenerated into actual fictions, there's that old line about "the best way to tell the truth is with fiction", and I now feel much more favorably inclined towards oral histories as perhaps at least as good, if not better, as their written cousins.
If you are not expected to remember everything like the ancient Greek were, you are not training your memory as much and it will be worse than if you did.
Now do I think it’s fair to say AI is to what reading/writing as reading/writing was to memorizing? No, not at all. AI is nothing near as revolutionary and we are not even close to AGI.
I don’t think AGI will be made in our lifetime, what we’ve seen now is nowhere near AGI, it’s parlor tricks to get investors drooling and spending money.
Not exactly.
We have accounts from figures who became famous by going against popular opinion, who aired those thoughts. It probably was not the mainstream belief, in that place, at that time. Don't try and judge Ancient Greece by Socrates or Plato - they were celebrities of the controversial.
Why not force everyone to start from first principles then?
I think learning is tied to curiosity and curiosity is not tied to difficulty of research
i.e. give a curious person a direct answer and they will go on to ask more questions, give an incurious person a direct answer and they won't go on to ask more questions
We all stand on the shoulders of giants, and that is a _good_ thing, not bad
Forcing us to forgo the giants and claw ourselves up to their height may have benefits, but in my eyes it is way less effective as a form of knowledge
The compounding force of knowledge is awesome to behold, even if it can be scary
It's like the struggle that we've all had when learning our first programming language. If we weren't forced to wrestle with compilation errors, our brains wouldn't have adapted to the mindset that the computer will do whatever you tell it to do and only that.
There's a place for LLMs in learning, and I feel like it satisfies the same niche as pre-synthesized Medium tutorials. It's no replacement for reading documentation or finding answers for yourself though.
LLMs will definitely be a technology that widens the knowledge gap at the same time that it improves access to knowledge. Just like the internet.
30 years ago people dreamed about how smart everyone would be with humanity's knowledge instantly accessible. We've had wikipedia for a while, but what's the take-up rate of this infinite amount of information? Most people prefer to scroll rage-bait videos on their phones (content that doesn't give them knowledge or even make them feel better, just that makes them angry)
Of course it's amazing to hear every once in a while the guy who maintains a vim plugin by coding on his phone in Pakistan.... or whatever other thing that is enabled by the internet by people who suddenly have access to this stuff. That's not an effect of all humans on average, it's an effect on a few people who finally have a chance to take advantage of these tools.
I heard in a YouTube interview a physicist saying that LLMs are helping physics research just because any physicist out there can now ask graduate-level questions about currently published papers, that is, have access to knowledge that would have been hard to come by before, sharing knowledge across sub-domains of physics by asking ChatGPT.
This echoes sentiments from the 2010s centered around hiring. Companies generally don’t want to hire junior engineers and train them—this is an investment with risks of no return for the company doing the training. Basically, you take your senior engineers away from projects so they can train the juniors, and then the juniors now have the skills and credentials to get a job elsewhere. Your company ends up in the hole, with a negative ROI for hiring the junior.
Tragedy of the commons. Same thing to day, different mechanism. Are we going to end up with a shortage of skilled software engineers? Maybe. IMO, the industry is so incredibly wasteful in how engineers are allocated and what problems they are told to work on that it can probably deal with shortages for a long time, but that’s a separate discussion.
An employee who does not do the effort to re-peg their labor time to market rates for their skill level is implicitly consenting to a prior agreement (when they were hired).
I wonder how things might change if short-term capital gains tax (<5 years) went way up.
When I started work (this was in the pre-consumer-internet era), job hopping was already starting to be a thing but there was defintely still a large "old school" view that there should be some loyalty between employer and employee. One of my first jobs was a place where they hired for potential. They hired smart, personable people and taught them how to program. They paid them fairly well, and gave annual raises and bonuses. I was there for about 8 years, my salary more than doubled in that time. Maybe I could have made more elsewhere, I didn't even really look because it was a good environment, nice people, low stress, a good mix of people since not everyone (actually only a few) were Comp. Sci. majors.
I don't know how much that still happens, because why would a company today invest in that only to have the employee leave after two years for a higher salary. "They should just pay them more" well yeah, but they did pay them in the sense of teaching them a valuable skill. And their competitors for employees started to include VC funded startups playing with free money that didn't really care what it cost to get bodies into the shop. Hard to compete with that when you actually have to earn the money that goes into the salary budget.
Would the old school approach work today? Would employees stay?
Long, long ago, the compact was that employees worked hard for a company for a long time, and were rewarded with pensions and opportunities for career advancement. If you take away the pensions and take away the opportunities for career advancement, your employees will advance their careers by switching companies—and the reason that this works so well is because all of the other companies would rather pay more to hire a senior engineer rather than take a risk on a junior.
It’s a systemic problem and not something that you can blame on employees. Not without skipping over a long list of other contributing factors, at least.
The math remains simple: if you already have an employee on your payroll, how in the world are you not willing to pay them what they can get by switching at that point? That's literally just starving one's own investment.
The real issue is that the companies who were "training" the juniors were doing so only because they saw the juniors as a bargain given that they were initially willing to work for the lower wage. They just don't stay that way as they grow into the craft.
- given context c, i tried idea a, b and c. where there other options that I miss ?
- based on this plan, do you see missing efficiency ?
etc etc
i'm not seeking answers, i'm trying to avoid costly dead ends
A LOT of the time the things I ask LLMs for are to avoid metaphorically wading through a garbage dump looking for a specific treasure. Filtering through irrelevant data and nonsense to find what I'm looking for is not personal development. What the LLM gives back is often a very much better jumping off point for looking through traditional sources for information.
Specifically, asking a question and getting an answer is not a general path to learning. Being asked a question and you answering it is. Somewhat, this is regardless of if you are correct or not.
> you're going to learn much more with the latter approach than the former
that the downside is a lack of deep knowledge that would enable better solutions in the long term