What are the threats to the AI boom?
economist.com
economist.com
I would guess, as with self driving, the initial sprint to 95% functionality is low hanging and requiring only a few 100 billion, but the last 5.9999% that makes it more usable than a human expert is a several more trillion dollars of spend away. That human expert only costs me $250,000/yr and I can hire him by the 6 min increment. We will get there though.
Still I don't think this is going to be enough to kill the energy for it (humans are confident and wrong quite often as well). But this should be guiding how it's adopted and regulations surrounding it. For example, it shouldn't be used to make any decisions itself, rather just helping a human who is ultimately responsible for applying judgement, with their decisions necessarily rebuttable/appealable. Although given our past several decades of "computer says no" and "policy says no", I'm not so hopeful on this front.
I think the sticking point is that, continuing with real estate, a very programmatic task I encounter is 'write a contract to purchase XYZ property at ABC address with the following requirements...' Not too different from spitting out some lines of code. An attorney does that for me, he will start with an old contract we've used in the past, and it takes his team 30 minutes to an hour, at a blended rate of $500/hr. So that starts to set an upper bound on the value of work to be performed, in one of the higher per-hour cost lines of work out there.
When you really drill down on the question of "but how will it generate value," it is relegated to being an efficiency tool for many and a replacement for google search for most. The smart will get more effective and powerful with better information, the less intelligent will be running off inaccurate or old info.
1. That's fairly obvious to people that have used it. I mean I guess there are a lot of people that have never used ChatGPT and think it doesn't bullshit, but those people aren't behind the AI boom.
2. There are loads of applications where 100% accuracy isn't required (even if it would be nice!). Obvious example is GitHub Copilot. It saves me a ton of time overall even if I often have to fix its mistakes and bullshit.
3. I imagine like half of the AI research community is working on fixing this. And they don't need to get it to 0% bullshit, just less bullshit than humans (which tbf is sometimes a low bar!)
Less bullshit than humans is a terrible goal.
If you take the real estate industry examples, you want less bullshit than a really good real estate reference book, not less bullshit than random people on reddit.
"which tbf is sometimes a low bar!"
Your 2nd example is a handy one to pick through. What % of your time is spent writing code compared to other work tasks? How much of that time did Copilot save? How much time do you have to spend fixing and validating (be honest, as if your quarterly bonus depends on it)? Whats your hourly rate? Whats the real cost of the high quality, cutting edge LLM (not the VC subsidized 'price').
And then.. does that mean you get to go to lunch early or didnt have to stay late? (no savings to your company). Or did it knock out a week's worth of work? What prevents Copilot from taking over your role completely ( I doubt it is anywhere close to that, you likely do a lot more than type out code, still requires the correct input and output validation, understanding goals with nuance and uncertainty)
So then in real dollars, it's a useful, potentially very expensive tool. which is great. Is it $48B great (for google)? Maybe! But thats a big bet!
I admit I do not understand the concept of researchers programming their models to be more accurate as questions progress from easy to esoteric, from 2+2=4 to "Should I avoid drinking corn syrup?" (ahem advertisers would like a word!) "which religion is better, A or B?" I dont see a way for AI to be unbiased and clean, and therefore trustworthy and useful at face value.
Probably 50%?
> How much of that time did Copilot save?
Unfortunately I can't use it at work, but I use it in my free time and I'd say it increases productivity by something like 10-50% depending on the task. 10% is more common of course, but given how expensive developer time is a Copilot subscription is worth it if it increases my programming productivity by like 0.5% which it easily exceeds.
> And then.. does that mean you get to go to lunch early or didnt have to stay late? (no savings to your company). Or did it knock out a week's worth of work?
My company would reap most of the benefit because they are paying me hourly.
> What prevents Copilot from taking over your role completely ( I doubt it is anywhere close to that,
Yeah basically it's nowhere near smart enough. Needs several orders of magnitude more intelligence before it could fully replace me. At that point I think society will have bigger problems because it will have replaced 90% of white collar jobs in general; not just programming.
> I dont see a way for AI to be unbiased and clean, and therefore trustworthy and useful at face value.
This also eliminates humans.
> Needs several orders of magnitude more intelligence before it could fully replace me.
I think this is true for many applications of AI, where everyone assumes everyone elses job is screwed, but the expert looks under the hood and says 'well isn't that cute. delete it and start over.'
I predict an AI product bloodbath as users and companies remember that we hold computers to an extraordinarily high standard of accuracy.
In terms of 25 productivity, yeah, I think that is a good thing and could be true. However, the problem is that it is sold under the pretense that you can fire a large chunk of workers and rely in large part on AI, which makes it a whole lot more attractive to investors.
Yes just a matter of recognizing it’s 25% of various job titles, not 100% of 25% of job titles
Or alternatively, drastically lower the skill requirement for the job and replace your skilled workers with cheap unskilled workers
If AI is just a tool that improves performance by 25% it changes what investors believe. It is a lot more expensive than 25% increase in productivity as investors were sold a different reality.
> Or alternatively, drastically lower the skill requirement for the job and replace your skilled workers with cheap unskilled workers
With the error rate of GenAI I'm curious to see how this plays out in the long run. It seems that only more skilled workers can pick up on the wrong answers.
Nearly all the wealth generated by productivity gains of the last 50 years has been pocketed by business owners and investors
https://assets.weforum.org/editor/HFNnYrqruqvI_-Skg2C7ZYjdcX...
My lesson from the dot com boom is that's it's very hard to pick the winners. For every Amazon, there were a thousand pets.com. Investing in DEC, Sun, Yahoo, and friends would not have paid off.
That's a broader lesson too. I have an okay -- far from perfect but decent -- track record knowing which national economies will grow, but investing in index funds for those economies rarely yields good returns. I have a tiny portion of my portfolio in various developing markets, and the GDP can rise tenfold with no investment gains.
Many very real revolutions have bubbles. Bubbles are the period when people over-invest.
When I allow myself to review the relevance and correctness of the attempted completion, I have to set aside the actual code that I was envisioning. This may or may not be easily recoverable after the (generally wrong) suggested completion is dismissed.
I am not neurotypical, and we are all different anyway, so it is obviously different for others. But I disabled the active code assistance option at work almost immediately due to it being actively harmful to maintaining flow state.
In other words, a bubble occurs when expectations >> reality.
Investors are throwing endless amounts of money to companies with a "dead end" product (current image Gen/LLM has a hard limit on improvement and only a whole new approach will surpass it).
Since the reality is that if we want real Ai we ought to invest in neuroscience and fields that can then convert to programmable logic (such as we can now create rudimentary emotions via logic gates).
A good approach is to plan on all things, from plateau to linear growth to polynomial to exponential to singularity, as within the realm of possibility.
Or to have the humility to acknowledge that we may be wrong.
What we do have is theory, hubris, potential, and speculation. And FOMO.
Genuine question- Have any AI product marketers slapped a price tag on their services that comes remotely close to the cost of providing that service?
Of course, the maddness of crowds would conclude the risk here is underinvestmemt.
When VCs sell their 2021-2023 bags, if the technology is still not yet economically self sustaining, it's over.
Or most probably, US national interests will step in, just like it did with SpaceX, and will provide any amount of billions required to keep in the fight, at least till China steps out of the AI race.
Beside the (naive?), naysayers, until the technology actually shows it is failing to keep the promise of super powerful AIs, it is a global, geopolitical race to get there (AGI/ASI), or till the point it fails (new AI winter).
To make quite sure it really doesn't work, and not dropping out of the race just to see a chinese ASI (Artificial Super Intelligence), emerge a couple of years later.
Anybody noticed already that any current or near future reveneu won't make a dench in the actual costs of running giant models, anyway, the models keep chugging along just fine. And the ("free") money keeps flowing in.
That content can be and is used to train models, effectively cancelling the "data-wall", bit by bit, all the time.
It really cannot.
Could you elaborate?