Others are saying this article is bearish, but then...
A huge amount of economic value is going to be created by AI. Company builders focused on delivering value to end users will be rewarded handsomely.
Such strong speculative predictions about the future, with no evidence. How can anyone be so certain about this? Do they have some kind of crystal ball? Later in the article they even admit that this is another one of tech's all-too-familiar "Speculative frenzies."
The whole AI thing just continues to baffle me. It's like everyone is in the same trance and simply assuming and chanting over and over that This Will Change Everything, just like previous technology hype cycles were surely going to Change Everything. I mean, we're seeing huge companies' entire product strategies changing overnight because We Must All Believe.
How can anyone speak definitively about what AI will do at this stage of the cycle?
How can anyone not see just how impactful it's going to be? Or already is? I can't think of a single recent technology that was so widely adopted by tech and non-tech people alike, immediately integrated into day-to-day experience. The rise of mobile phones and e-commerce in the 90s would be the last time I've seen this happen (I'm not counting smartphones, as those are more of an iteration). Or social media, in purely software space.
I've just had GPT-4o write me a full-featured 2048 clone in ~6 hours of casual chat, in between of work, making dinner, and playing with kids; it cost me some $4 in OpenAI bills, and I didn't write a single line of code. I see non-tech people around me using ChatGPT for anything from comparison shopping to recipe adjustments. One person recently said to me that their dietitian is afraid for their career prospects because ChatGPT is already doing this job better than she is. This is a small fraction of cases in my family&friends circle; anyone who hasn't lived under the rock, or wasn't blinded by the memetic equivalent of looking at a nuclear weapon detonation, likely has a lot of similar things to say. And all of that is not will, it's is, right now.
You can't know this for certain until you look back on it in retrospect. We did not know mobile phones and e-commerce were going to be huge back in the 90s. We know now, of course, looking back, and the ones who guessed right back then can pat themselves on the back now.
Everyone is guessing. I'll admit it's totally possible LLMs and AI are going to be as earth shattering as its boosters claim it will be, but nobody can know this now with as much certainty as is being written.
Correct, but the thing is, AI blown up much faster than phones - pretty much a decade in a single year, in comparison. Mobile phones weren't that useful early on, outside of niche cases. Generative AI is already spreading to every facet of peoples' lives, and has even greater bottom-up adoption among regular people, than top-down adoption in business.
What do you base that on though? Two years into the iPhone, Apple reported a $6.75b revenue on iPhone related sales. ChatGPT may reach or surpass that this year considering they're currently at $3.4b. That's not exactly what I would call growing faster than phones, however, and according to this article, very few people outside of nvidia and OpenAI are actually making big money on LLM's.
I do think it's silly to see this wave of AI to be referred to as the next blockchain, but I also think you may be hyping it a little beyond its current value. It being a fun and useful tool for a lot of things isn't necessarily the same thing at it being something that's actually worth the money investors are hoping it will be.
My childhood? I was a teen when mobile phones started to become widely used, and soon after pretty much necessary, in my part of the world. But, to reiterate:
That's just an iteration, and not what I'm talking about. Smartphones were just different mobile phones. I'm talking about the adoption of a mobile phone as a personal device by general population.
That's probably something which needs to be disentangled in these conversations. I personally don't care what investors think and do. AI may be hype for the VCs. It's not hype for regular Janes and Joes, who either already integrated ChatGPT into their daily lives, or see their friends doing so.
This is all just meaningless anecdotes.
And regular Janes and Joes are not using ChatGPT. Revenues would be 10-100x if that were the case.
3/4 of the people I know are actively using it are on free tier. And based on all the HN conversations in the last year, plenty of HNers commenting here are also using free tier. I'd never go back to GPT-3.5, but apparently most people find it useful enough to the point they're reluctant to pay that $20/month.
As for the rest, OpenAI is apparently the fastest-growing service of all time ever, so that says something.
I'm one of the free tier people.
A while back I used 3.5 to make a chat web page so I could get the better models as PAYG rather than subscription… and then OpenAI made it mostly pointless because they gave sufficient 4o access to the free tier to meet my needs.
Or they find it useless enough that they're unwilling to pay for the upgrade.
Calling the iPhone an iteration is pure nonsense. Mobile phones had tiny utility compared to smartphones.
A phone on the go didn’t fundamentally alter anything except for making coordination while traveling easier. I went through both the cell phone adoption curve and the smartphone curve.
The latter was the massive impact that brought computing to the remaining 85% of the planet and upended targeting desktop operating systems for consumers by default.
Calling smartphones an iteration on cellphones is like calling ChatGPT an iteration on the neural networks we had 10 years ago.
> Mobile phones had tiny utility compared to smartphones
They had tiny utility compared to modern smartphones but the first iPhone was a glorified iPod with a touchscreen and a cellular radio. It didn’t have an app store and the only thing it really did better than other mobile phones was web browsing, thanks to the touchscreen keyboard.
It wasn’t as revolutionary as hindsight now makes it seem. It was just an iteration on PalmPilots and Blackberries.
Another thing that is forgotten about the first iPhone: I think Apple negotiated with AT&T (?) to change the voice mail system so you could select from your iPhone (voicemail app?) which message you wanted to listen. Prior, you always needed to call the mobile provider voice mail system, then listen the messages in order (or skip them). That was a huge early selling point for the iPhone. I know -- no one cares about voice mail in 2024, but it used to be very important.
Its a lot easier to use AI when its basically given away for free than when it cost $399 for a Palm Pilot in the 90s.
For a $399 device, Palm Pilot did well and had an excellent reputation for the time. Phones really took over the PDA market as a personal pocket-computer more-so than being used as ... a phone...
Really, I consider the modern smartphone a successor to the humble PDA. I grew up in that time too, and I remember the early Palm adopters having to explain why PDAs (and later Blackberries) were useful. That was already all figured out by the time iPhone took over.
Isn't this a an odd take when you're discussing things on a VC website? In any case, if you like LLM's you probably should care considering it's the $10b Microsoft poured into OpenAI that's made the current landscape possible. Sure, most of those money were fuled directly into Azure because that's where OpenAI does all it's compute, but still.
Are they paying for it? If they aren't then will they pay for it? I think it's also interesting to view the numbers. ChatGPT had 1.6 billion visitors in January 2024, but it had 637 million in May 2024.
Again. I don't think it's all hype, I think it'll change the world, but maybe not as radically as some people expect. The way I currently view it is another tool in the automation tool-set. It's useful, but it's not decision making and because of the way it functions (which is essentially by being very good at being lucky) it can't be used for anything important. You really, really, wouldn't want your medical software to be written by a LLM's programmer. Which doesn't necessarily change the world too much because you really, really, didn't want it to be written by a search engine programmer either. On the flip-side, you can actually use ChatGPT to make a lot of things and be just fine. Because 90% (and this a number I've pulled out my ass, but from my anecdotal experience it's fairly accurate) of software doesn't actually require quality, fault tolerance or efficiency.
But the iPhone was launched more than 10 years past mobile phones (in fact, more than 20, but that's stretching it). There were more than 1B mobile phones shipped in 2006, the year before the iPhone launched.
The iPhone was not the first cell phone. Initial adoption of cell phones was much slower than ChatGPT. Think 1980’s/1990’s.
Even when cell phones started getting popular, often only one or two family members would get one. The transition time between “started becoming popular” and “everyone has one” was >5 years and even then it was relatively normal that people would just turn off their cell phone for a few days (to mixed reactions from friends and family).
Any talk of "regular people" inside the HN bubble is fraught with bias. Commenters here will sometimes tell you that "regular people" work at FAANG, make $400K/yr and have vacation homes in Tahoe. Actual "regular people" use Facebook occasionally, shop at the grocery store, watch Sportsball on TV, and plan their kids' birthday parties or their next vacation. They're not sitting there augmenting their daily lives with ChatGPT.
You're a long time HN contributor and I admit when I see your username, I stop and read the comment because it's always insightful, polite, and often makes me think about things in ways I never have before! But this discussion borders on religious fervor. "Every facet of peoples' lives?" Come on, man!
I'm aware of the HN bias, but in this case, I'm talking regular, non-tech, sportsball or TikTok watching crowd. Just within my closest circles, one person is using ChatGPT for recipes, and they're proficient at cooking so I was surprised when they told me the results are almost always good enough, even with dietary restrictions in place (such as modifying recipes without exceeding nutrient limits). Another person used it for comparison shopping of kids entertainment supplies. Another actually posted a car sale ad and used gen-AI to swap out background to something representative (no comment on ethics of that). Another is evaluating it for use in their medical practice.
(And I'm excluding a hundred random uses I have for it, like e.g. making colorbooks for my kids when they have very specific requests, like "dancing air conditioners" or whatever.)
Without data, we're trading anecdotes. Your circle is in the bubble, mine isn't. This user[1] shared a Pew survey, which looks like the best we're going to get. The survey asked what % of people ever used ChatGPT, which I'd interpret as "at least one time ever" and the number is less than 1 in 4. I'd love to see actual data on what percentage of people use it at least once daily, which is the bar I'd accept for "integrated with their daily lives."
1: https://news.ycombinator.com/item?id=40870205
Fair enough. My circle may be in a bubble, and come to think of it, selection bias is a factor - I can think of all the people close to me who use it, but there's more equivalently close friends and relatives who (as far as I know) don't. I do think the survey is giving quite a big results, given we're barely a year into the whole ChatGPT craze - but we can revisit this topic when more data becomes available. I'm sure it'll come up again :).
My dad used AI to generate artwork with pictures hung up across the house.
He's intersted in meditation and mindfulness. He's not a native English speaker, so he's using AI to help him write content. He's then using AI text to voice to turn his scripts into YouTube videos. The videos have AI generated artwork too.
My dad is a retired welder in his late 60s. He's as "regular people" as it gets.
I'm a high school teacher and GPT has completely changed teaching. We're using it to help with report writing, lesson planning, resource creation, even for ourselves to get up to speed on new topics quickly.
I'm working on a tool for teachers that's only possible with GPT.
It's by far the single, most transformative technology I've ever encountered.
Any evidence backing up these claims about adoption?
I thought the same about adoption (across multiple audiences, not just tech workers and/or young people), was faced with surprising poor knowledge about GenAI when making surveys about it in my company. Maybe investors are asking the same questions right now.
the article states
and links to
https://www.theinformation.com/articles/openais-annualized-r...
That's a lot of $20/month subscriptions. it's not all that but that's a lot of money, regardless.
OpenAI’s revenue is not exclusively subscriptions.
There are a lot of companies building private GPTs and using their API.
IIRC openai was the fastest growing service by subscriptions of all time.
Pew Research asked Americans this March:
https://www.pewresearch.org/short-reads/2024/03/26/americans...
23% said they'd used ChatGPT, 43% said they hadn't, 34% didn't know what it was.
Truck drivers, construction crew, couriers, lawyers, sales people of all kinds, stock brokers, ... Most of the economy that isn't at a desk 996. Pretty big niche, bigger than the non-niche possibly.
You are in a bubble.
Huh? Mobile phone was available commercially in 1980s, and started to proliferate in 1990s. It was definitely not popular in those groups early on. Any of them.
It was in Europe. I remember that people started to get them in the mid 90's and they were everywhere by the end of that decade.
You are in a bubble, hyping this up way too far
I personally attribute this FOMO to so called AI influencers who love "shilling" AGI as something that's as true as 1 + 1 = 2
I don't get why people insist this is agi any more than a ship is artificial general swimming.
It doesn't matter if it's general, what matters is that its useful. And if you don't find it useful just remember a lot of people in the 00s didn't find google useful either since they already had the yellow pages.
I strongly suggest paying for a subscription to either openai or anthropic and learning quickly.
You don't even have to do that, just go to http://ChatGPT.com and type at it. you don't even need to make an account.
You get what you pay for, despite what everyone is saying the 4o gpt model is really bad for long form reasoning.
Buy the subscription and use the turbo4 model.
After that api credits so you get access to the playground and change the system prompt. It makes a huge difference if you don't want to chat for 10 minutes before you get the result you want.
Learning what quickly exactly?
Swimming and 'intelligenting' are definitely not in the same category.
You don't need earth shattering though. The PC revolution was huge because every company got a bit more productive with things like word processors and printing and email.
The internet (and then later mobile) was big because every company got a revenue boost, from a small one with online presence to a a huge one for e-commerce to transformative with Netflix and streaming services.
Ignoring the more sci-fi claims of AGI or anything, if you just believe that AI is going to make every office worker 10% more productive, surely each company is goign to have to invest in AI, no? Anytime you have an industry that can appeal to every other company, it's going to be big.
I wouldn't be surprised if in large companies (say >500 office workers) 10% of all office work becomes redundant. Not in the form of each worker getting 10% more productive, but in form of some roles getting eliminated completely and others losing 80% of their workload.
Why? You could make the same argument about PCs or mobile or the Internet?
Eh? We did. The whole dot-com boom was predicated on that assumption. And it wasn't wrong. But most of the dot-com investments went sideways. In fact, they imploded hard enough to cause a recession.
In the same vein, even if we all agree that AI is fundamentally transformative, it doesn't mean that it's wise to invest money into it right now. It's possible that most or all of these early products and companies will go bust.
I think this is the right sentiment. I know a handful of AI startups that have raised 10's to 100's of millions. All of them were crushed with gpt-3 and subsequent models. None of them have any real revenue, have crazy burn rates with their compute spend, and generally haven't proven any value with their AI platforms. Most seem to be working on tech to find a problem rather than the inverse. Funds are throwing money on ideas.. that haven't panned out for years now. I worked with one and they were spending 10's of millions per researcher on AI compute... which makes sense if it's directed but most of the researchers were just running off on their own and the company hoped one would figure something out. Very disorganized for the stacks of cash being spent. Similar things have happened in the Cyber Security field just at a lesser scale.
But hey, Nvidia is investing in companies.. to spend money on Nvidia .. infinite money glitch!
That's his point, we _know_ this. People can already use OpenAI as a replacement for Google search and people are already doing this. You might not think this is a good thing yadda yadda go to the library, but we already know that chat bots are here to stay.
There is a huge spectrum between "here to stay" and "changing everything". On another note, I think if the people arguing here would work out quantitative predictions, they would find that a not insignificant part of the "disagreement" about how big we should expect this to really be is in the framing.
Except AI is already being used by people (like myself) every day as part of their usual work flow - and it's a huge boost in productivity.
It's not IF it will make an impact - it IS currently making an impact. We're only just moving past early adopters and we're still in the early stages in terms of tooling.
I'm not saying that AI will become sentient and take over humanity, but to think that AI isn't making an impact is to really have your head in the sand at this point.
Being able to create 2048 in 6 hours has basically zero economic value.
Can ChatGPT materially and positively impact the code written by big companies? Can it do meaningful work in excel? Can it do meaningful PowerPoint work? Can it give effective advice on management?
Right now we don’t know the answer to those questions. LLM apps can still improve in many ways - better base models, better integration with common enterprise applications, agentic processes, verifiability and so on - so there is definitely hope that there will be significant value created. Companies and people are excited because there’s huge potential. But it is really just potential right now … current systems aren’t creating real enterprise value at this moment in time
I know the answer to the first three. Yes, yes, and yes. I've done them all, including all of them in the past few weeks.
(Which is how I learned that it's much better to ask ChatGPT to use Python evaluation mode and Pandoc and make you a PPTX, than trying to do anything with "Office 365 Copilot" in PowerPoint...)
As for the fourth question - well, ChatGPT can give you better advice than most advice on management/leadership articles, so I presume the answer here is "Yes" too - but I didn't verify it in practice.
Yes, they are. They would be creating even more value if not for the copyright and exports uncertainty, which significantly slows enterprise adoption.
You say this but from a management perspective at a large enterprise software company I have not seen it.
Some of our developers use copilot and gpt and some don't and it is incredibly difficult to see any performance difference between the groups.
We aren't seeing higher overall levels of productivity.
We aren't seeing the developers who start using copilot/gpt rush ahead of their peers.
We aren't seeing any ability to cut back on developer spend.
We aren't seeing anything positive yet and many developers have been using copilot/gpt for >1 year.
In my opinion we are just regaining some of the economic value we lost when Google Search started degrading 5-10 years ago.
You can't measure productivity for shit, otherwise companies would look entirely differently. Starting from me not having to do my own finances or event planning or hundred other things that are not my job description, not my specialty, and which were done by dedicated staff just a few decades ago, before tech "improved office productivity".
That's because individual productivity is usually constrained by team productivity. Devs rushing ahead of their teammates makes the team dysfunctional.
Devs aren't stupid. They're not going to give you an opportunity if they can avoid it.
My belief is that's because you aren't measuring the right things. But then, no one is. This is a problem well-known to be unsolved.
We can't measure small changes and we aren't great at comparing across orgs.
However, at the director level we can certainly see a 50% or 100% productivity improvement in our teams and with individuals in our teams.
We aren't seeing changes of this magnitude because they don't exist.
There are other potential explanations.
Perhaps developers are now slacking off.
Perhaps we have added more meetings because developers have more free time.
Or perhaps developers were never the bottleneck.
We can see large productivity improvements when we make simple changes like having product managers join the developers daily standup meetings. We can even measure productivity improvements from Slacks/Zooms auto-summary features. Yet gpt/copilot doesn't even register.
While not code generation, this auto-summary is powered by the same tech. I think using it to sift through and surface relevant information, as opposed to generation of new things, will have the biggest impact.
By far the greatest value I get out of LLMs is asking them to help me understand code written by others. I feel like this is an under-appreciated use. How long has this feature been in Copilot? Since February or so? Are people using it? I do not use Copilot.
Now that's dangerous thinking, but I think you are onto something.
I use ChatGPT copilot etc to reduce my cognitive load and get a lot of things done quicker so I also have more time to fuck around. You're out of your goddamn mind if you think I'm going to increase my output for the mere chance that maybe I'll get an above inflation raise in a year. "We gave our devs a magic 10% productivity boost machine, but their output hasn't increased? I guess the machine doesn't work..." It's amusing how out of touch you are.
There is an ethical question in here that I don’t have an answer for. As an employee, I find a way to do my job more efficiently. Do I hand those efficiencies to my employer so I can get a pat on the head, or do I keep them to myself to make my own life less stressful? If I give them to the boss, do they even have the ability to increase my pay? Using the extra time to slack off rather than enriching the employer might be the best choice.
Edit: and now I see chillfox made the same point.
Out of curiosity what are you using to measure developer productivity platform or metrics wish (if beyond typical sprint metrics)?
Writing code is just one part of the process. Other bottlenecks might prevent you from seeing overall productivity improvements.
For example:
- time between PRs being created and being picked up for review and merged
- time spent on releasing at end of sprint cycles
- time spent waiting for QA to review and approve
- extreme scrum practices like "you can only work on things in the sprint, even if all work is done"
How are you measuring developer productivity? Were those that adopted copilot and chatgpt now enabled to finally keep up with their faster peers (as opposed to outstrip them)? Is developer satisfaction improved, and therefore retention?
Yes, other bottlenecks might be preventing us from seeing overall productivity improvements. We might require large organisational changes across the industry in order to take advantage of the improvements.
I guess we will see if smaller startups without many of our bottlenecks are suddenly able to be much more competitive.
We use a host of quantitative and qualitative measures. None of them show any positive improvements. These include the basics like roadmap reviews, demo sessions, feature cycle time, etc as well as fairly comprehensive business metrics.
In some teams every developer is using copilot and yet we can't see any correlation with it and improved business metrics.
At the same time we can measure the impact from changing the label on a button on our UI on these business metrics.
No.
No.
Those are very high level. If there's no movement on those, I'd guess there are other things bottlenecking the teams. They can code as fast as possible and things still move at the same pace overall. Nice thing to know.
If you want to really test the hypothesis that Copilot and ChatGPT have no impact on coding speed, look at more granular metrics to do with just coding. The average time from the moment a developer picks up a work item to the time it gets merged (assuming code reviews happen in a timely fashion). Hopefully you have historical pre-AI data on that metric to compare to.
Edit: and average number of defects discovered from that work after merge
We do collect this data.
I personally don't put a lot of stock in these kinds of metrics because they depend far too much on the way specific teams operate.
For example perhaps Copilot helps developers understand the codebase better so they don't need to break up the tasks into such small units. Time to PR merge goes up but total coding time could easily go down.
Or perhaps Copilot works well with very small problem sizes (IMO it does) so developers start breaking the work into tiny chunks Copilot works well with. Time to PR merge goes way down but total code time for a feature stays the same.
For what it is worth I do not believe there have been any significant changes with these code level metrics either at the org level.
Is it the superstars or the line holders that have been the first adopters? I could speculate, but I am actually curious what you are seeing in practice.
The first adopters seem to be the same group / personality type that is always first to adopt new technologies.
Very few of these are the superstars. But plenty are good solid senior developers.
Passing on personal productivity gains to management is always a HUGE L for the individual worker.
As a dev, you can use the saved time to slow down and not be stressed, spend more time chatting with colleagues, learn new skills, maybe improve the quality of the code, etc. Or you can pass it on to management which will result in your workload being increased back to where you are stressed again and your slower colleagues will be let go, so now you get to feel bad about that and they won't be around to chat with.
I have never in my life seen workers actually get rewarded with pay raises for improved productivity, that is just a myth the foolish chase, like the pot of gold at the end of the rainbow.
I have also tried being the top performer on a team before (using automation tools to achieve it), and all I got was praise from management. That's nice, but I can't pay for my holidays with praise, so not worth it.
You think we are antsy worker bees, hastily rushing forwards to please the decision maker with his fancy car?
You are leadership. It's not hard. Cui bono, follow the money, etc. The incentives are clear.
If me and my peers were to receive a magic "do all my work for me" device I can assure you exactly zero percent of that knowledge will reach your position. Why would it? The company will give me a pat on the back. I cannot pay with pats on the back. Your Tesla cannot be financed with pats on the back. Surely you understand the nature of this issue.
If you write a spaghetti system where collecting the context for the AI is a big time sink, and there are so many service/language barriers that AI get confused, of course AI is going to suck. Of course, if you give your programmers a game pad and tell them to use it to program with a virtual keyboard, they're gonna suck ass too, so you should consider where the fault really lies.
Can you elaborate on what this saved over just making the ppt the old fashioned way?
"I have this set of notes attached below; would you kindly group them by X and tabulate, and then use Python with Pandoc to make me a PowerPoint with that table in it, plus an extra slide with commentary from the notes?"
Attach notes, paste, press Enter, wait half a minute, get back a PPTX you can build on, or just restyle[0].
Sure, it's faster to build the presentation yourself than to make ChatGPT make the whole thing for you. But the more time-consuming and boring parts, like making tables and diagrams and summaries from external data or notes, is something ChatGPT can do in a fraction of time, and can output directly into PPTX via Pandoc.
(There's a lot of fun things you can do with official ChatGPT and Python integration. The other day I made it design, write and train a multi-layer perceptron for playing tic-tac-toe, because why waste my own GPU-seconds :).)
--
[0] - In contrast, if you make the same request in PowerPoint's O365 Copilot, it'll barf. Last time I tried, it argued it has no capability to edit the document; the time before that, it made a new slide with text saying literally "data from the previous message".
I think you’re thinking about things very locally. Of course ChatGPT can help with some coding - I use it for regex quite often cause I never really learned that well.
The problem is that at the average medium sized company code looks like this - you have 1mln lines of code written over a decade by a few hundred people. A big portion of the code is redundant, some of it is incomplete, much of it is undocumented. Different companies have different coding styles, different testing approaches, different development dynamics. ChatGPT does not appreciate this context.
Excel has some similar problems. First of all Excel is 2 dimensional and LLMs really don’t think in 2 dimensions well. So you need to flatten the excel file for the LLM. A common approach to do this with LLMs is using pandas and then using the column and row names to index into the excel.
Unfortunately, excels at companies cannot be easily read using pandas. They are illogically structured, have tons of hardcoding, intersheet referencing is weird circular ways and so on. I spent some time in finance and sell side equity research models are written by highly trained financial analysts and are substantially better organized than the average excel model at a company. Even this subset of real world models is far from suitable for a direct pandas interpretation. Parsing sell side models requires a delicate and complex interpretation before being fed into an LLM.
My friends at McKinsey say that while it can’t fine-tune reports and presentations with quite enough nuance, it does a good job sifting through lots of shit to pick out important parts they should pay more attention to, highlighting data/talking points that contradict a working hypothesis, assisting in writing emails, and other time-consuming or very nit-picky tasks.
That said, no one I know has fed it real customer data, that would be a career-ending event. But self-hosted models like Gemma2 open up the possibility for using LLMs against real customer info.
Hard to tell if that says more about the value of LLMs or the lack of value of McKinsey...
Can it do meaningful PowerPoint work
Yes, it absolutely can. I threw together a PowerPoint presentation with a script for a low-value, high visibility meeting a couple of weeks ago with ChatGPT 4.0 and a PowerPoint plugin. Everyone loved it.
Corporate values. Drew the short straw, but had to present something.
Yes it can.
But more importantly have you tried ChatGPT Data Analyst?: https://openai.com/index/improvements-to-data-analysis-in-ch...
It drops the barrier for "pretty good data analysis" to effectively zero.
Canva and Figma are both building this and they are pretty decent right now. Better than most PowerPoints I've seen.
The aforementioned Data Analyst does good presentations in a different way, too.
Yes. Unfortunately can't talk about this except it is mindblowingly good.
And on top of that, it can do PowerPoint presentations too - magic keywords are "use Python and Pandoc".
It already has at a Fortune 100 company I contract with currently.
We can quibble about what "meaningful" means, but it satisfactorily answered questions for two friends about how to build formulas for their datasets and is currently being used to summarize data insights from a database at a different large client (Excel =/= database, but the point stands).
I've used Midjourney multiple times a month to generate base imagery for various things in PowerPoint (usually requires modification in Photoshop, but saves me several hours each time compared to digital painting or photobashing from scratch).
Again, what does "effective" mean in the context of management? I've seen VP-level individuals with hundreds of people in their orgs using AI tools for different things.
It really feels like a significant chunk of the HN crowd is living in a bubble with respect to AI in the real world right now. It's absolutely invading everything. As for how much revenue that will translate into long-term vs. the investment dollars being poured into it, that's a more interesting question to discuss.
That's actually a really good point. In the realm of programming, things that were previously not done because they were too expensive can now be done. Prior to ChatGPT, GP could have a) done it themselves, but the cost was too high/it wasn't worth their time, b) found enough time to write a spec, found someone on upwork/etc, paid them to make it, except that costs money they didn't want to spend, or c) just not do it. Now, GP can code this thing up while watching netflix with the kids or whatever. What programs do not exist that previously did not have the economic value to exist, but now can, thanks to programming time getting cheaper?
Now apply that to fields outside of programming. LLMs' ability to program is front and center here, since many of us can program, but they do other things as well.
Okay I guess I've just had a different experience entirely. Maybe I'm jaded by hallucinations.
The code ChatGPT generates is often bad in ways that are hard to detect. If you are not an experienced software engineer, the defects could be impossible to detect, until you/ChatGPT has gone and exposed all your customers to bad actors, or crash at runtime, or do something terribly incorrect.
As far as other thought work goes, I am not consulting ChatGPT over, say, a dietician or a doctor. The hallucination risk is too high. Producing an answer is the not the same as producing a correct answer.
If your procees is asking it to "write me all this code", then you slap it in production, you're going to have a bad time. But there's intermediate ground.
Do you know any doctors, by chance? You have way more faith in experts than I do.
ChatGPT is just statistically associating what it’s observed online. I wouldn’t take dietary advice from the mean output of Reddit with more trust than an expert.
Doctors can be associating what they’ve learned, often with heavy biases from hypochondriacs and not enough time per patient to really consider the options.
I’ve had multiple friends get seriously ill before a doctor took their symptoms seriously, and this is a country with decent healthcare by all accounts.
Human biases are bad too.
So true. And it's hard to question a doctor's advice, because of their aura of authority, whereas it's easy to do further validation of an LLMs diagnosis.
I had to change doctor recently when moving towns. It was only when chancing on a good doctor that I realised how bad my old doctor was - a nice guy but cruising to retirement. And my experience with cardiologists has been the same.
Happy to get medical advice from an LLM though I'd certainly want prescriptions and action plans vetted by a human.
By the time a doctor paid me enough attention to realise something was wrong I had suffered a spinal cord injury whose damage can never be reversed. I’m not falling all over myself to trust chatgpt, but I got practically zero for doctors either. Nobody moved until I threatened to start sueing.
I sometimes use ChatGPT to prepare for a doctor's visit so I can have a more intelligent conversation even if I may have more trust overall in my doctor than in AI.
I’ve seen so many doctors advertising or recommending homeopathic “medicines” or GE-132 [1], that I would be fairly more confident in an LLM + my own verification from reliable sources. I’m no doctor, but I know more than enough to recognize bullshit, so I wouldn’t just recommend this approach to everyone.
[1] https://pubmed.ncbi.nlm.nih.gov/1726409/
You realize that "online" doesn't just mean Reddit, but also Wikipedia and arXiv and PubMed and other sources perused by actual experts? ChatGPT read more academic publications in any field than any human.
I keep hearing this, but it's incorrect. While I only know R, which is obviously a simple language, I would never type out all my code and go without testing to ensure it does what I intended before using it regularly.
So I can't imagine someone that knows a more complex language just typing out all of it before integrating it into business systems at their work or anything else before testing it.
Why would AI be any different?
Why the hell are AI skeptics acting like getting help from an LLM would involve not testing anything? Of course I test it! Why on earth wouldn't I? Just as I tested code made by freelancers I hired on commission before using the code I bought from them. Do AI skeptics really not test their own code? Are you all insane?
Take it from someone who started with R, R is 100% not a simple language. If you can write good R, you're probably a surprisingly good potential SE as R is kinda insane and inconsistent due to 50+ years of history (from S, to R etc).
Hmmm.. I'm trying to imagine interviewing for SE and telling them I got wealthy from a crypto market-making algorithm I coded in R during Covid and the interviewer responding with anything but laughter or with silence as they ponder legal ways to question my mental health.
It's an excellent language, I think, for many reasons. One is that you can work with data within hours because even before learning what packages or classes are, you got native objects for data storage, wrangling, and analysis. Even import my Excel data and rapidly learn the native function cheat sheet so fast that I was excited to learn what packages are because I couldn't wait to see what I could do.
That was my experience in like 2010, maybe, and after having C++ and Python go in and out my head during college multiple times. I view R as simple only because I actually felt more helpless to keep learning it than helpless to ever learn coding at all. Worth noting that I was a Stat/Probability tutor with a Finance degree and much Excel experience.
Ah yeah, makes sense. That's the happy path for learning R (know enough stats etc to decode the help pages).
That being said, R is an interesting language with lots of similarities to both C based languages and also Lisp (R was originally a scheme intepreter), so it's surprisingly good at lots of things (except string manipulation, it's terrible at that).
I wonder about this a lot, because there's a future here where a decent amount of software engineering is offloaded to these AIs and we reach a point, in the near future, where no one really knows or understands what's going on. That seems bad. Put another way, suppose that your primary care doctor is really just using MedAI to diagnose and recommend treatment for whatever it is you went in to see him about. Over time, these sorts of shortcuts metastasize and the doctor ends up not really knowing anything about you, or the other patients, or what he's really doing as a doctor ... it's just MedAI (with whatever wrongness rate is tolerable for the insurance adjusters). Again, seems bad. There's a palpable loss of human knowledge here that's enabled by a "tool" that's allegedly going to make us all better off.
The closest analogy here is that we don't have as full-featured autopilots in airplanes as we could, because they reduce safety.
Right, good point. Maybe I'm making an argument that some features, or scope of features, should be highly regulated along the same lines.
I agree. I've just seen it hallucinate too many things that on the surface seem very plausible but are complete fabrications. Basically my trust is near 0 for anything chatgpt, etc. spits out.
My latest challenge is dealing with people that trust chatgp to be infallible, and just quote the garbage to make themselves look like they know what they are talking about.
LLMs are language model, it's crazy people expect them to be correct in anything beyond surface level language.
Yeah, I was probably being a bit too harsh in my original comment. I do find them useful, you just have to be wary of the output.
I recently needed to help a downstream team with a problem with an Android app. I never did mobile app dev before, but I was able to spin up a POC (having not coded in Java for 22 years) and solve the problem with the help of ChatGPT 4.0.
Sure I probably would have been able to do it without ChatGPT, but it was so much easier to have something to bounce ideas off-of. A safety net, if you will.
The hallucination risk was irrelevant: it did hallucinate a little early on. I told it it was a hallucinating, and we moved onto a different way of solving the problem. It was easy enough to verify it was working as expected.
Seems to me this is the equivalent of fast retrieval and piecing together from a huge amount of examples in the data. This might take far more time if you were to do this yourself. That's a plus for the tools. In other words, a massively expensive (for the service provider) auto-complete.
But try to do something much more simple but has much fewer examples (a typical case is something which has bad documentation) in the data, and it falls apart. I even tried to use Perplexity to create a dead simple CLI command, and it hallucinated an answer (looking at the docs, it misused the parameter, and may have picked up on someone who gave an incorrect answer in the data.)
Easy answer. Ask ChatGPT to write testable code, and tests for the code, then just verify the tests. If the tests don't work, have ChatGPT use the test output to rewrite the code until it does.
If you can't have ChatGPT write testable code because of your architecture, you have other problems. People with bad process and bad architecture saying AI is bad because it doesn't work well with their dumpster fire systems, 100% facepalm.
There exist lots of reasons why code is hard to test automatically that have nothing to do with the architecture of the code, but with the domain for which the code is written and runs.
Does it work though, yes it does. There are many human coders who write bad code and life goes.
I've seen both the good and the bad. I really like the good parts. Most recently, Claude Sonnet 3.5 fixed a math error in my code (I prompted it to check for it from a well-written bug report, and it did it fix it ever so perfectly).
These days, it is pretty much second nature for me to pull up a new file & prompt Copilot to complete writing the entire code from my comment trails. I don't think I've seen as much change in my coding behaviour since Borland Turbo C -> NetBeans.
It's already gotten significantly better and faster in a few yrs. Maybe LLMs will hit a wall in the next 5yrs but even if it does it's still extremely useful and there are always other ways to optimize the current technology where this is already a major development for society.
My experience actually agrees with you. It's just that the set of use cases that either:
- Are hard (or boring) to do, but easy to evaluate - for me, e.g. writing code, OCR, ideation; or
- Don't require a perfectly correct answer, but more of a starting point or map of the problem space; or
- Are very subjective, or creative, with there being no single correct answer,
is surprisingly large. It covers pretty much everything, but not everything for everyone at the same time.
This is not meant to be an offense, but you are in a bubble. The vast, vast majority of people do not use LLMs in their day-to-day life. That’s ok, we’re all in our own bubbles.
You should also post the 2048 clone as proof. Lots people saying they built X in Y minutes with AI. But, when it’s inspected, it’s revealed it very obviously doesn’t work right and needs more development.
I posted it twice already in this thread, but I guess third time's the charm: http://jacek.zlydach.pl/v/2048/ (code: https://git.sr.ht/~temporal/aider-2048).
It's definitely not 100% correct (I just spotted a syntactic issue in HTML, for example), and I bet a lot of people will find some visual issue on their browser/device configuration. I don't care. It works on my desktop, it works on my phone, it's even better than the now-enshittified web and Android versions I used to play. I'm content :).
It is too large for my phone display (iphone SE). Do you think chatgpt can fix it?
Yes. It does so trivially, but in the process it breaks the CSS for larger screens. I couldn't get it to get both to work at the same time in 5 minutes of trying. My modern CSS skills aren't good enough to quickly notice what the problem is, so it's beyond my horizon of caring (but I do encourage hints I could pass on to the AI).
So, no, it doesn’t fix it trivially. Also isn’t correctly sized on iPhone 11 Safari.
It does fix it trivially, just in a way that causes regression on larger screens :).
As mentioned above, I don't care. It's sized correctly for the devices I use to play it, and I'm not going to put any more work into this. I mean, even junior web devs get paid stupidly high salaries for doing Responsive Web Design; I ain't gonna work on this for free.
(But I will accept AI-generated patches, or human-generated advice I could paste into the prompt to get a correct solution :P.)
I hand-wrote perhaps 10-20 lines of this project:
https://github.com/williamcotton/guish
The rest is Claude 3.5 (with a dash of GPT-4o) with a LOT of supervision!
I'd say I'm about 8 hours deep and that this would have taken me at least 30+ hours to get it to the current state of polish.
I used it to make some graphs at work today!
Quite interesting — but how is it fundamentally more productive than being in VS code in R or python? You don’t get any of the benefits of an IDE here. I often find myself doing very similar workflows but default to either VS Code or the shell. Trying to imagine this truly making workflows faster/easier/more efficient, but can’t figure it.
Maybe it isn’t? I am just experimenting with new UX! Maybe it could be integrated into an editor of… the fuuuturrre!
But seriously, do you have any thoughts or suggestions?
I've heard this asserted sometimes, and I just don't think it's true. ChatGPT's use cases as consumer software were discovered basically immediately after GPT 3 came out, and nothing new has really emerged since then. It's great for automating high school/undergrad B-quality writing and the occasional administrative email. Beyond that, it sometimes does better than 2024 Google(though probably still worse than 2019 Google) on knowledge questions.
ChatGPT is software. The barrier to entry is almost zero, and the tech industry has had decades of practice in enticing people into walled gardens and making sure they can never leave. If it's not completely taken over the world in the time it's had, I wouldn't bet on it doing so without a massive jump in capability or accuracy.
Scientific computing is going to be overhauled with this. We have essentially standardized a way to approximately solve optimization problems. And people are now going to design methods to fit this kind of solution, just like people did with linear solvers.
Not to mention all the proof-writing that will become simpler with this optimization/searcher now.
Sure, AI represents a substantial improvement over other heuristic methods in some areas. But that's a long way down from the "AI is going to be a permanent fixture in most people's day to day lives" claim that the tech industry is betting the farm on right now.
I think you’re missing that “every” K-12 student is using ChatGPT for all their work right now. Yes, the state of education is in peril (with ChatGPT actually being pretty far down the pareto chart), but the generation coming up after us is absolutely growing up using LLMs the way we used calculators, and using it for everything.
We may not see universal adoption in people who are currently >30 but I think we will in the generations that are <25 now.
But are they using it for anything else? Even GPT-2 was "good enough" for high school writing assignments. The fact that this power user class of young people hasn't found any other use cases for LLMs despite vast improvements in LLM capability doesn't reflect well on the merits of LLMs as consumer software.
Why are you assuming they haven't? High school writing assignments and homework are the majority of the problems teenagers face daily, but they're also having fun with it, and why wouldn't they try it on new problems as they come along?
Of course students are using ChatGPT. It helps them to write assignments.
But it isn't translating into better across the board test results and at least in Australia we would be able to tell because we have yearly standardised testing.
And so schools are looking at it as more of a form of cheating and simply moving back to in-person, hand-written tests.
with what? how? there's already a lot of bad python/R code out there, how more of it will "overhaul" scientific computing?
.. what does this mean? we had simplex solvers before. do you mean things like protein folding prediction?
I canceled my GPT-4 subscription recently because it just wasn't that impactful for me. I found myself using it less and less, especially for things that matter (because I can't trust the results). The things it's good at: boilerplate text, lightweight trivia, some remixing/brainstorming. Oh and "write me a clone". Yes, it can write clones of things, because it's already seen them. I've spent WAY more time trying to get anything useful out of it for a non-clone project, than it took me when I just buckled down and did it.
Yes, "many things are clones", but that just speaks to how uncreative we are all being. A 2048 clone, seriously? It was a mildly interesting game for about 3 minutes in 2014, and it only took the original author a weekend to build in the first place. Like how was that impactful that you were able to make another one yourself for $4?
It's been my "concentration ritual", an equivalent of doodling, for a few years in 2010s, so I have a soft spot for it. Tried getting back to it the other day, all my usual web and Android versions went through full enshittification. So that $4 and couple hours bought me a 2048 version that's lightweight, works on my phone, and doesn't surveil or monetize me. Scratched my own itch.
Of course, that's on top of gaining a lot of experience using aider-chat, by setting myself a goal of making a small, feature-complete app in a language I'm only moderately good at (and environment - the modern web - which I both hate and suck at), with extra constraint of not being allowed to write even a single line of code myself. I.e. a thing too boring for me to do, but easy enough to evaluate.
And no, the clone aspect wasn't really that important in this project. I could've asked it for something unique, and I expect it to work more-less the same way. In fact, this is what I'm trying right now, as I just added persistent state to the 2048 game (to work around Firefox Mobile aggressively unloading tabs you're not looking at, incidentally making PWAs mostly unusable) and I have my perfect distraction completely done.
EDIT:
BTW. did I ever tell you about the best voice assistant ever made, which is Home Assistant's voice assistant integrated with GPT-4o? I have a near-Star Trek experience at my home right now, being able to operate climate control and creature comforts by talking completely casually to my watch.
(Also a Chat GPT4o,x etc user)
Try asking it something actually technologically hard or novel and see what answers you get.
In my experience, it repeatedly bails out with "this is hard and requires a lot of careful planning" regardless of how much I try to "convince" the model to live the life of a distributed systems engineering expert. Sure, it spits out some sample/toy code... that often doesn't/compile or has obvious flaws in it.
Very few people are working on technologically hard or novel things. Those people have always had very special effects on society, and will continue to be special going forward - LLM's aren't going to prevent those people from delivering real value. HN has an absurdly rich concentration of these special people, which is why I like it. And many of them are surrounded by other similarly special people in real life. I only regularly talk with maybe 1-2 people in real life who are even close to that type of special. Even when I was a chemical/electrical/petroleum engineer working closely with other engineers - usually only 1-2 people at each workplace were doing really smart work.
That said, the majority of my friends are doing relatively manual work (technician, restaurants, event gigs, sex work) and are neither threatened by LLMs nor find much use for them.
You are likely not wrong.
Even though I do think that almost any profession can potentially find use for LLMs in some shape or form. My opinion is LLMs can increase productivity and be a net positive the way the Internet/search engines are if used correctly.
To expand on my original comment: All that being said, I think the hype/media cycle overestimates the magnitude of the potential positive LLM effect. You’ll see numbers like 5x, 10x, 100x increase in productivity thrown around. If I have to bet, I would say the likely increase is going to be in the 1x-1.5x range but not much greater.
Most things in the world are not infinitely exponential, even if they initially seem to be.
(Not sure why the downvotes.)
Appreciated! But if we fret about a few downvotes, we're using the forum wrong. Some unpopular views need to be discussed - either because they hold some valuable truth that people are ignorant of, or because discussing them can shine a light on why the unpopular views are misguided. I suspect the downvotes are related to "Very few people are working on technologically hard or novel things" -- many HN users have been surrounded since elementary school by tons of people who do currently work on hard or novel problems, so they understandably think that >5-10% of people do that, when in fact it's closer to maybe 1-in-200. I've been part of social groups who went to high schools with absurd numbers of Rhodes' Scholars and peer groups where everyone in the group can trivially get through medical schools with top marks, receive faculty positions as professors at top-3 universities, found incredible startups through insane technical competence, and still all think they're stupid because they compare themselves to the true 1-in-a-million geniuses they grew up with who are doing research so advanced that it's far beyond their most remote chances of ever having even surface-level comprehension of that research. Their extended social group likely comprises >1% of all Americans working on "hard or novel problems", but since 75% of them are doing it, they have no idea that the real base rate is closer to 1-in-200, generously. They grossly underestimate their relative intelligence vs. the median and grossly overestimate the ability of average people (and explain away differences in outcome to personality issues like "laziness").
There are a surprising number of people from these peer groups on HN. These are the people who will never be threatened by LLM's -- they are capable of adapting to use any new tools and transcending any future paradigm, save war/disease/famine.
Yours is a very reasonable take that I wouldn't argue against. I also think it's reasonable that some people think it will be 5x-100x -- for the work some individuals are familiar with it very well might be already, or they might be more bullish on future advances in reinforcement learning / goal-seeking / "search" (iterative re-search to yield deep solutions).
I reactively feel this is stretching it for people who travel around just to load/unload boxes of equipment at events/concerts/etc. But the way you worded this is definitely not wrong - even manual laborers may find LLM's useful for determining whether they, their peers, and their bosses are following proper safety/health/HR regulations. More obviously, Sex workers will absolutely be using LLM's to screen potential customers for best mutual-fit and maintain engagement (as with lawyers who own small practices, a large number of non-billable hours goes towards client acquisition, as well as retention). LLM's are not "there" yet for transparent personalized client engagement which maintains the personality of the provider, but likely will be soon with some clever UX and RAG.
What’s a 2048 clone?
http://jacek.zlydach.pl/v/2048/
This version seems to be using slightly different rules: my recollection is that the original 2048 prevented a move if it wouldn't cause any blocks to shift or collapse, while this one spawns a block unconditionally.
It doesn't anymore.
You're right. I pasted your comment to aider and it fixed it on the spot :).
EDIT: see https://git.sr.ht/~temporal/aider-2048/commit/9e24c20fc7145c....
A bit lazy approach, but also quite obvious. Pretty much what you'd get from a junior dev.
(Also if you're wondering about "// end of function ..." comments, I asked the AI to add those at some point, to serve as anchors, as the diffs generated by GPT-4o started becoming ambiguous and would add code in wrong places.)
I think it's also not progressing the block size with score: IIRC the original came also begins spawning 8s and 16s once you get above your first 512 block. But I could be misremembering.
(This kind of feedback driven generation is one of the things I do find very impressive about LLMs. But it's currently more or less the only thing.)
I don't remember it doing progressive block sizing - I only vaguely remember being mildly annoyed by getting to 2048 taking >2x the effort it took to get to 1024, which itself took >2x the effort of getting to 512, etc. - a frustration which my version accurately replicates :).
Honestly I'm totally in the AI camp but 6 hours to make a 2048 clone?! And that's a good result? Come on.
The vast majority of devs would take much longer than 6 hours if this task were assigned to them. Yes, the vast majority of devs might suck, but its actually a shockingly low percentage who are genuinely skilled. You are probably surrounded by rare skilled devs.
you come on. it's not six hours of focused work, it sounds like six hours while watching Netflix and puttering around.
Of casual chatting in between making dinner, doing chores, playing with kids, and work. The actual time spent strictly on this was more like 2 hours. And that's with me writing zero lines of code, and using the technology stack (modern web) I hate and suck at (but I'm proficient enough to read the basics).
Also, to be honest, it would've been much faster if GPT-4o didn't occasionally get confused by the braces, forcing me to figure out ways to coerce it into adding code in the right place. This is to say, there's still plenty of low-hanging fruits for improvement here.
Just because it's impactful doesn't mean it's going to make much money. Take audiobooks for example, the market is valued at around $5 billion. In the very near future audiobooks will be created with AI. Does that mean anybody is making billions with those AI audiobooks? I don't think so. The value of audiobook will go towards $0 and audiobooks as a product category will completely disappear. Audiobooks will simply be the text2speech feature of your eBook reader.
Similar stuff will happen with a lot of other content, things that used to be costly will become very cheap. And then what? The amount of books people can consume doesn't scale into infinity. Their entertainment needs will be served by auto-generated AI content. Even the books themselves will be written by AI sooner or later.
Advertising industry might also start hurting badly, as while they will certainly try getting ads into AI content, users will have AI at home to filter it out. A lot of classic tricks and dark pattern to manipulate the user behavior will no longer work, since the user has a little AI helper to protect them from those tricks.
I don't doubt that the impact of AI will be gigantic, but a lot of AI produced content won't be worth anything, since it's so easy to create for everybody. And there isn't much of a moat either, since new models with better capabilities pop up all the time from different companies. Classic lock-in is also not really usable anymore, as AI can effortlessly translate between different APIs and user-interfaces.
Why would I watch someone else's AI content when I can watch my own with about the same work? It probably takes longer to pick something on Netflix than to shout at my TV to show me some auto-generated on the fly show about whatever I'm feeling like watching right now. Can even be building upon itself "show me yesterday's show but in space and give it some new twists that I don't expect".
You can share opinions about a common show the next day with another person and bond over it. For example, about a football match.
While I agree that most audiobooks will be priced near zero, there will still be a healthy market for highly paid actors to read, like Morgan Freeman. That said, it will be a tiny segment in the market, and many famous people will need to fight/sue AI providers to remove impersonations. I expect those markets will be flooded by fan-created favourites that read like Morgan Freeman.
Meanwhile I have the opposite experience.
I have used chatgpt less and less, and bar copilot which is a useful autocomplete I just don't have much use for AI.
I know I'm not alone, and even though I've seen many people super excited by Dall-E first and chatgpt later they use very rarely both of them.
This is where I am with it now. I got bored with the image generators and tired of their plastic looking output.
I still use GPT or Claude occasionally but I find switching over to prompting breaks my mental flow so it’s only a net win for certain kinds of tasks and even there it’s not a huge step up from searching Stack Overflow.
For me it's been great for things like generating a latex version of my cv, or CSS for a web app. It's worth an OpenAI subscription for me, but I do wonder if it's worth all the energy and resources gone into making those GPU clusters and powering them.
The huge up-front energy costs of GPU clusters, both of making and operating them, is amortized over all subsequent uses of the models, i.e. inference. Inference itself is cheap per query. Your use cases aren't consuming that much energy. I feel the amount is in the same ballpark as what you'd use doing those things yourself.
As for whether it's worth it, I argue this is the single most useful application of GPUs right now, both economically and in terms of non-monetary value delivered to users.
(And training them is, IMO, by far the most valuable and interesting part of almost all creative works available online, but that's another discussion.)
I don't think the uptake of LLMs by non-technical people has been that dramatic. Nobody in my familial or social circles (which spans doctors, lawyers, artists, waitresses, etc.) has really mentioned them outside of asking my what I think about them.
As for what I think about them: I've been impressed with some aspects of code generation, but nothing else has really "wowed" me. Prose written with the various GPT models has an insincere quality that's impossible to overlook; AI-generated art tends to look glossy and overproduced in the same way that makes CGI-heavy movies hard to watch. I have not found that my Google Search experience was made better by their AI experiments; it made it harder, not easier, for me to find things.
While I absolutely agree that many movies over-use CGI, even with the relative decline in superhero movies, CGI-heavy movies still top the box office. Going over the list of highest-grossing movies each year [0], you have to go back about three decades to find a movie that isn't CGI-heavy, so apparently they're not that difficult for the general public to watch.
[0] https://en.wikipedia.org/wiki/List_of_highest-grossing_films
True. It's also a rude reality that much of the US uses word art and comic sans to advertise things, so I might just be a snob. Then again, impressing the snobs is a relevant part of the mass adoption curve :-)
May we see it?
http://jacek.zlydach.pl/v/2048/
https://git.sr.ht/~temporal/aider-2048
There's a full transcript of the interactions with Aider in that repo (which I started doing manually before realizing Aider saves one of its own...).
Before anyone judges quality of the code - in my defense, I literally wrote 0 lines of it :).
That works surprisingly well! I mean that I am genuinely surprised.
I agree with this take.
The perspective I take is the 15 year view: The iPhone 1 sucked objectively but by the iPhone 3 the trajectory was clear and 15 years later the world is a very different place.
You hear people very focused on specific shortfalls: "I asked it to write code and look it made a mistake". But there are very clear routes to fixing these and there are lots of people finding it useful despite these bugs.
I think AI is bigger than mobile. I'm nearly 50, and I remember the PC boom, the Internet boom, Social Networking boom, Mobile boom, SaaS boom - probably more that I forget.
I think the PC and Internet booms are the only ones that are as impactful as AI will be in 15 years.
Maybe mobile is as big, maybe not - depends if someone can build AI devices that replace the UX of phones sometime in the next 15 years.
Interesting takeaway if this holds true - following the analogy, people will be excited about GPT-16 as much as they are excited about iPhone 16 today, compared to the excitement about first few generations of iPhone. That may mean that LLMs may become so ubiquitous that we won't even notice. Which means there will be no AGI.
If GPT-16 is pretty much GPT-4 but a bit faster and a bit smarter, then sure. If subsequent generations of GPT continue the trend of qualitatively improving on the predecessors, then we'll likely hit AGI somewhere around GPT-10 at the latest.
I think it’s reasonably obvious that the tech could have a lot of potential, but that’s yet to be realised. Chatbot interfaces are so primitive and clearly not the final form for LLMs, but people have to invent that.
But, tech being impactful doesn’t mean it will create and deliver value for others.
Can you come up with an example of tech that was impactful without delivering value to others?
The closest I can think of would be the atom bomb, but even that arguably brought significant value in terms of relative geopolitical stability.
Sorry, I phrased it poorly.
Very often technology advancements redistribute value, taking it away from many people and allowing companies to capture more profit and/or lower costs. Such as self-serve checkouts at supermarkets leading to less cashier jobs.
Companies will acrue value, but 'common folk' will lose it.
I've just had GPT-4o write me a full-featured 2048 clone in ~6 hours of casual chat, in between of work, making dinner, and playing with kids;
As cool as this might be, what is the actual economic value of this? 2048 is free, you didn't even have to spend a dollar to get it.
I don't give a damn about economic value of the game - I just wanted to have a 2048 that's lean and not powering the adtech economy. At the same time, I care about both economic and non-monetary value of me leveraging AI tools in creating software, so this game was a perfect little project to evaluate aider-chat as a tool.
The questions stands though: Is building all this infra so you (random internet guy) can generate a 2048 clone worth the increased emissions and the $600B of investment?
This kind of example always confuses me. I don't see the value vs reading an article like this: https://www.freecodecamp.org/news/how-to-make-2048-game-in-r...
If I said I built a 2048 clone by following this tutorial, noone would be impressed. I just don't see how reading a similar tutorial via a chat interface is some groundbreaking advancement.
The difference is that with a chat interface you can ask it questions, you can brainstorm with it, you can drill down into specific concepts and you can do it any language/framework, without waiting for a tutorial to be written for it. And it would help you do the "homework" at the end.
For the tutorial you linked to, there's a lot of prior knowledge assumed, which the author alludes to in the summary, which a chat interface would help with:
This time I decided to focus on the essence of the topic rather than building basic React and CSS, so I skipped those basic parts. I believe it makes this article easier to digest.
can you show the 2048 clone?
EDIT : My bad, I see you posted the link elsewhere (link for posterity http://jacek.zlydach.pl/v/2048/ )
TBH 6 hours seems a lot longer than I would have expected.
6 hours of on-and-off casual chatting while working, making dinner, playing with kids, etc. Total time spent in front of keyboard was at most half of that. And would be half of that still, if I had more experience guiding GPT-4o/Aider diffs out of stupid ruts - experience which I gained through this short project.
Also: 6 hours is a lot if you sit down to it and know exactly what to write. Not when you half-remember how the game works, don't have uninterrupted focus time for it, and deal with executive function issues to boot.
Keep in mind, the "hole" takes ChatGPT and Copilot/Gemini/etc into account at a rate of $5-10B per product. The remainder of $500B is about the size of the global cell phone market. Where will it come from?
And we seem to be in an exponential upswing of hardware capacity, so we expect extreme improvements from an already impressive base. >90% of the compute that can be used to train models doesn't exist yet.
The article explains a $600B gap in AI revenue expectations vs actuals. Rather than addressing that, you're posting about some dietician you know who worries their job will get replaced, as if it has any relevance whatsoever to the big picture.
We should have seen massive revenue growth and raises in future quarter revenue forecasts in the most recent round of SaaS company earnings reports. I think tons of companies have hyped up AI as if it's just on the cusp of AGI. The lack of massive top line growth has proven we're not even close, but the enormous investor speculation these companies triggered is the main reason for this $600B gap.
I'm not at all saying AI won't be transformational because it definitely does bring revolutionary capabilities that weren't possible before.
This is baffling to me, because these are two use cases I have tried and in which ChatGPT completely fails to produce any useful information.
GPT-4 is an upgrade over a search engine (on the 2010 internet, which was much easier to search than the internet today) and there is certainly opportunity in using it to chain together complex programming tasks fully programmatically, but we are stuck right on the cliff of a truly reliable and generalizable AI, and it isn't clear that we can just train an AI with the next generation of GPU's and more data and bridge that gap. Most of the truly high value add activities (fully autonomous programs and AGI that creates novel inventions) rely on a model more intelligent and more consistent than the current state of the art. So I think most of the valuation is in speculative potential.
No offense, but you’re group is just very early on the adoption curve and is not representative of at all.
I have a bunch of peers that haven’t used chatgpt at all and they are software developers. A bunch more tried it once, realized how terrible it was for anything you aren’t already knowledgeable in and then haven’t gone back to it.
Recipe adjustments has to be a joke unless they are really basic things like “cut it in half”. ChatGPT is terrible at changing recipes without fundamentally changing them and will offer multiple “substitutions” for an ingredient that have extremely different outcomes that it doesn’t warn or know about.
This is the type of thing that I’ve only ever read about on the internet and have never heard of a normal person actually doing.
I actually don't think it's going to be that impactful. It often gets everything wrong. Today I asked ChatGPT questions about a game I used to play. It got it all wrong. I've recently asked questions about coding. A majority of it is also wrong. I've tried using it in a professional setting and it's also hit or miss.
I'm not saying AI is useless but it's certainly not the panacea that some say it is.
I think that comes down to the kind of people you hang out with.
While I use AI quite often, none of my friends or family does. A few of them will use an image gen once or twice a year. And at work, only a few of my colleagues use AI.
So my impression is that current gen AI is too hard to use correctly, has too many rough edges and is not useful enough for most people.
Progress also seems to have stalled around the GPT-4 quality. Everything after GPT-4 (GPT-4 Turbo, GPT-4o, Claude 3 Opus, Claude 3.5 Sonnet) seems to be pretty much producing the same quality output, I have been using Open WebUI to bounce around between the different models and I can't really tell a difference in the quality of them, they are all roughly the same for my use case (programming/sysadmin stuff).
So the question of if a plateau has been hit or if scale can still improve quality is real to me.
Which technology are you talking about? ;-)
What I can clearly say is that I know no one from my social circles and extended social circles who used these AI chatbots for anything else than "simply trying out what is possible" (and ridiculing the results). A quote from a work colleague of me when I analyzed the output of some latest generation AI chatbot: "You shouldn't ask so complicated questions to the AI chat bots [with a huge smile on his face]. :-)"
One difference between AI and mobile is this.
The mobile revolution needs three kinds of investment:
(A) The carrier has to build out a network
(B) You need to buy a handset
(C) Businesses need to invest in a mobile app.
The returns that anybody gets from investing in A, B or C depend on the investments that other people have made. For instance, why should I buy a handset if the network and the apps aren't there? Why should a business develop an app if the network and users aren't there? These concerns suppress the growth of mobile phones in the early phase.
ChatCPT depends on the existing network and existing clients for delivery so ChatGPT can make 100% of the investment required to bring their product to market which means they can avoid the two decades of waiting for the network and handsets to be there in order to motivate (C).
---
Note another thing that younger people might never have noticed was that the US was far behind the rest of the world in mobile adoption from maybe 1990 to 2005. When I changed apartments in the US in the 1990s I could get landline service turned on almost immediately by picking up the phone. When I was in Germany later I had no idea I could go into a store in most countries other than the US and walk out with a "handy" and be talking right away so I ended up waiting a month for DT to hook up my phone line.
The evidence is all around you. For anyone who has made any serious attempt to add AI to your current life and work process, you will fairly quickly notice that your productivity has doubled.
Now, do I as a random software engineer who is now producing higher quality code, twice as fast, know how to personally capture that value with a company? No. But the value is out there, for someone to capture.
It already is changing everything, in multiple fields. Go look up what happened to the online art commission market. It got obliterated over a year ago and is replaced by people getting images from midjourney/ect.
Furthermore, if you are a software engineer and you haven't included tools like github copilot, or cursor AI into your workflow yet, I simply don't consider you to be a serious engineer anymore. You've fallen behind.
And these facts are almost immediately obvious to anyone who has been paying attention in the startup space, at least.
That sounds like you're fresh out of college. Copilot is great at scaffolding but doesn't do shit for bug fixing, design, or maintenance. How much scaffolding do you think a senior engineer does per week?
40+ years of coding here. I've been using LLMs all day and getting a large boost from it. That last thing I did was figure out how to change our web server to have more worker processes. It took a half dozen questions to cure a lot of ignorance and drill down the right answer. It would have taken a lot longer with just a search engine. If you're not seeing the large economic advantage of these systems you're not using them like I am.
I think that one of the reasons there's a surprising amount of pushback because a lot of developers don't like the sort of collaborative, unstructured workflow that chat-oriented tools push onto you.
I once worked with someone who was brilliant, but fell apart when we tried to do pair-programming (acturial major who had moved into coding). The verbal communication overhead was too much for him.
This is a really interesting observation that makes a lot of sense to me. I can relate to this and it really helps to explain my own skepticism about LLMs "helping" with programming tasks.
I've always thought of software development as an inherently solo endeavor that happens entirely inside of one's own mind. When I'm faced with a software problem, I map out the data structures, data flows, algorithms and so on in my mind, and connect them together up there. Maybe taking some notes on a sheet of paper for very complex interactions. But I would not really think of sitting down with someone to "chat" about it. The act of articulating a question "What should this data structure look like and be composed of?" would take longer than it would take to simply build it and reason about it in my own brain. This idea that software is something we do in a group socially, with one or more people talking back and forth, is just not the way I operate.
Sure, when your software calls some other person's API, or when your system talks to someone else's system, or in general you are working on a team to build a large system, then you need to write documents and collaborate with them, and have this back-and-forth, but that's always kind of felt like a special case of programming to me.
The idea of asking ChatGPT to "write a method that performs a CRC32 on a block of data" seems silly to me, because it's just not how I would do it. I know how to write a CRC function, so I would just write it. The idea of asking ChatGPT to help write a program that shuffles a deck of cards and deals out hands of Poker is equally silly because before I even finished writing this sentence, I'm visualizing the proper data structures that will be used to represent the cards, the deck, and players' hands. I don't need someone (human or AI) to bounce ideas off of.
There's probably room for AI assistance for very, very junior programmers, who have not yet built up the capability of internally visualizing large systems. But for senior developers with more experience and capability, I'd expect the utility go down because we have already built out that skill.
I consider myself to be fairly senior, and use it all the time for learning new things. I work with some brilliant senior developers who lean on it heavily, but I do think it doesn't mesh with the cognitive styles of many.
There might be something to this; however, N=1, I'm very much the kind of developer who hates pair-programming and falls apart when forced to do it. But it's not the conversation that's the problem - it's other people. Or specifically, my fight-and-flight response that triggers when I am watched and have to keep up with someone (and extreme boredom if the other person can't keep up with me). LLMs aren't people, and they aren't judging me, so they do not trigger this response.
Chat interface is annoying, though. Because it's natural language, I have to type a lot more, which is frustrating - but on the other hand, because it's natural language, I can just type my stream of thought and the LLM understands it. The two aspects cancel out each other, so in terms of efficiently, it's a wash.
I just read the manual.
do you flip to the back of the book to the index to find the pages that references a topic, or do you use ctrl-f?
I also use the table of contents.
Nope. 10 years experience working at startups and FAANG.
And yes cursor AI/copilot helps with bugs as well.
It works because when you have a bug/error message, instead of spending a bunch of time on Google/searching on stack overflow for the exact right answer, you can now do this:
"Hey AI. Here is my error message and stack trace. What part of the code could be causing it, and how should I fix it".
Even for debugging this is a massive speed up.
You can also ask the AI to just evaluate your code. Or explain it when you are trying to understand a new code base. Or lint it or format it. Or you can ask how it can be simplified or refactored or improved.
And every hour that you save not having to track down crazy bugs that might just be immediately solvable, is an hour that you can spend doing something else.
And that is without even getting into agents. I haven't figured out yet how to effectively use those yet, and even that is making me nervous/worried that I am missing some huge possible gains.
But sure, I'll agree that of all you are doing is making scaffolding, that is a fairly simply usecase.
That's not how I work since I stopped being a junior dev. I might google an error message/library combination if I don't understand it but in most cases, I just read the stacktrace and the docs, or maybe the code.
I don't doubt that LLMS can be quite useful when working with large, especially foreign, codebases. But I have yet to see the level of "if you don't use it you're not an engineer" some people like to throw around. To the contrary, I'd argue if you rely on an LLM to tell you what you should be doing, you aren't an engineer, you are a drone.
depends on what you're working on. i'm a senior engineer currently doing a lot of scaffolding for startups and my copilot saves me a ton of time. life's good.
It's getting better and new UIs for it are being tested like Claude and artifacts.
Sr. Eng adopted copilot and sung it's praises a lot faster then the jr engineers. Especially when working on codebases with less familiar languages.
I started teaching myself programming 40 years ago and I believe that Copilot and other AI programming tools are now an essential part of programming. I have my own agent framework which I am using to help complete some tasks automatically.
Maybe take a look at tools like aider-chat with Claude 3.5 Sonnet. Or just have a discussion with gpt-4o about any programming area that you aren't particularly familiar with already.
Unless you literally decided you learned everything you need and don't try to solve new types of problems or use new (to you) platforms ever..
Ive added "how have you incorperated generative AI into your workflow" as an interview question, and I dont know if it is stigma or actual low adoption, but I have not had a single enthusiastic response across 10+ interviews for senior engineer positions.
Meanwhile, I have chatGPT open in background and go from unaware to informed for every new keyword I hear around me all day everyday. Not to mention annotating code, generating utlity functions, and tracing errors
What, in your mind, is the right answer to that question?
Good question.
The best answer is for someone who has found ways to boost their own productivity, but also understands the caveats such as hallucinations and not pasting proprietary information into text boxes on the internet.
Would you consider hiring on a contract basis? I use AI tools like Copilot in vim, and have my own agent framework to ask questions or even edit files for me directly, which I have been trying to use more. And I could use a new contract. You can see my email in my HN profile.
I think if sort of depends on the work you do. If you’re working on a single language and have been for a while then I imagine that much of the value LLMs might give you already live in your existing automation workflows.
I personally like co-pilot but I work across several languages and code bases where I seriously can’t remember how to do basic stuff. In those cases the automatic code generation from co-pilot speeds my efficiency, but it still can’t do anything actually useful aside from making me more productive.
I fully expect the tools to become “necessary” in making sure things like JSdoc and other domination is auto-updated when programmers alter something. Hell, if they become good enough at maintaining tests that would be amazing. So far there hasn’t been much improvement over the year we’ve used the tools though. Productivity isn’t even up across teams because too many developers put too much trust into what the LLMs tell them, which means we have far more cleanup to do than we did in the previous couple of years. I think we will handle this thing once we get our change management good enough at teaching people that LLMs aren’t necessarily more trustworthy than SO answers.
Very true! From where I sit, most of the hype cycles were overestimated in the short term & underestimated in the long term: world wide web, mobile, big data, autonomous cars, AI, quantum, biotech, fintech, clean tech, space and even crypto.
It's a bit weird to claim that "quantum", autonomous cars, and crypto are "underestimated". If anything they've been overhyped and totally failed to deliver actual value.
Underestimated in the long term. It's become clear that the time constant for deep-tech innovation & adoption doesn't match the classic VC-backed SaaS adoption curve.
We're nearing general quantum supremacy for practical jobs within the next 5 years. Autonomous cars are now rolling out in select geographies without safety drivers. And crypto is literally being pushed as an alternative for SWIFT among the BRICS, IMF, and BIS as a multi-national CBDC via the mBridge program.
Twice as fast is way over exaggerating the reality. In certain cases, sure, but more generally you are looking at 10%-50% productivity increase, more likely on the lower end. I say this as someone who has access to ChatGPT and AI code completion tools and use them every day, and the numbers are backed up by Google's study. https://research.google/blog/ai-in-software-engineering-at-g...
Yeah some expert I work in the field type YouTubers I remember well over 7 months ago now kept saying that we're going to have AGI within 7 months. He was like the big prediction hinging practically his whole channel on... Sorry I don't have the name but there's a little anecdote.
Whenever I hear the AI hype cycle, I'm always reminded of expert systems and how they were going to revolutionize the world.
You should also be reminded about the internet. After the dotcom bubble it was extremely common to hear it outright dismissed as never being useful for anything.
Sometimes the future just gets here before we're ready for it.
Repeat after me: An LLM is not AI. The internet enabled a whole new world of possible applications. It's unlikely this or even the next upgrade to ML will get there. If we get to AGI, sure, that's grpund-breaking, but we're still a few steps removed from that.
Repeat after me: "AI hype" is not like Bitcoin hype, it's like Dot-com boom.
Generative models are already changing how people live and work. Ignore the grifters, and ignore the entrepreneurs. Look at civilians, regular folks, and watch how it impacts them.
Oh the belief is so strong! Wouldn't it be great if this were true.
It's not hard to have strong beliefs about something that's real.
To be fair to Bitcoin, the world's largest economic bloc is actively building an alternative to USD/Western banks for international settlements, with blockchain technology.
It doesn't have anything to do with the libertarian vision of BTC but it's the same technical concept.
I see you saying this all over this topic and yet I don't know anyone who uses AI for anything real. I've never used it once for anything, and I work in tech. The only people I know who use AI are kids who use it to do their homework for them. I'm sure they love it, but it's hardly a good thing.
It's like hiring someone to go to the gym for you, as far as I can see.
They are more intelligent than the average person I deal with on a daily basis.
The one thing us meat bags have going for us is that we have bodies and can do things.
No they aren't. They are differently intelligent. Which includes being more "intelligent" in some ways, and vastly less in others.
It is an all too common mistake to infer an LLM's capability based on what it would mean if a human could produce the same output. But it's not the same thing at all. A human that could produce the quality of many LLM outputs I've seen would be a person with superintelligence (and supercreativity, for that matter). But a human who is constrained to only the output that an LLM is capable of would be an untrustworthy idiot.
What's interesting to me is looking at the human+LLM combos that have recently entered the world. (As in, everyone who is regularly using good quality LLMs.) What are they capable of? Hopefully they'll be at least as intelligent as a regular human, though some of the articles on people blindly accepting LLM hallucinations do make me wonder.
And with the advances in robotics recently, who knows how long we're going to hold on to this monopoly.
Good luck with that genie
eBay, Amazon, Google, Yahoo etc were all around at the time and making serious money.
Not sure who those people were but it was very obvious to most that the internet was here to stay.
there were probably people who doubted electricity, vaccines and indoor plumbing.
There were also people who doubted the Segway, Magic Leap, Theranos, 3D TVs, Windows Phone, and Google Glass.
I think doubt is OK, at least it is before any particular technology or product has actually proven itself.
the gap between participants in this conversation is that some have had it proven itself for themselves and others around them, and others have not seen that same proof.
Thing is, that comment would have worked in a thread three years ago about ‘metaverses’. Sometimes, the early adopters are the only adopters. Not saying that’s definitely the case here, but it is looking like it’s going in that direction - huge hype, absurd levels of VC spending, a bunch of ultra-enthusiastic early adopters, but real mainstream application still absent.
This is false equivalence and you know better. Electricity is foundational technology. What we call AI are LLMs, which are great and useful, but not in the same league as something foundational like electricity.
Now, the question of "Are LLMs intelligent" is debatable, but intelligence is foundational itself.
The ration of hyped technologies that turned out to be overhyped, versus ones that turned out to be as impactful as electricity is... I don't even know how many orders of magnitude different, but it's a lot.
This really is different, and I say that as someone who spent a lot of time on expert systems, not as someone who is overly bought into AI hype.
The problem with expert systems is that even if the tooling was perfect the people using them needed a rather nuanced and sophisticated understanding of ontologies. That just wasn’t going to happen. There is not enough of that kind of expertise to go around. Efforts to train people largely failed. I think the intentional undermining of developer salaries pushed a lot of smart people out of the software industry making the problem even worse.
That’s what makes AI special, the ability to deliver value even when used by unsophisticated operators. Many workflows can largely stay the same and AI can be sprinkled in where it makes the most sense. I use it for documentation writing and UI asset production and it’s better in that role than the people I used to pay.
If, like me, you're using LLMs on a daily basis and getting real personal value out of them, it's hard not to conclude that they're going to have a big impact.
I don't need a crystal ball for this. The impact is already evident for us early-adopters, it's just not evenly distributed yet.
That's not to say they're not OVER hyped - changing the entire company roadmap doesn't feel like a sensible path to me for most companies.
Early Adopters are always True Believers. That's why they are early adopters. Every single one of them is going to say "The impact is clear! Look around you! I use XYZ every day!" You really don't know what the adoption curve will look like until you get into the Late Majority.
Early adopters were using gpt-2 and telling us it was amazing.
I used it and it was completely shit and put me off openai for a good four years.
gpt-3 was nearly not shit, and 3.5 the same just a bit faster.
It wasn't until gpt-4 came out that I noticed that this AI thing should now be called AI because it was doing things that I didn't think I'd see in decades.
I tried GPT-2 and thought it was interesting but not very useful yet.
I started using GPT-3 via the playground UI for things like writing regular expressions. That's when this stuff began to get useful.
I've been using GPT-4 on an almost daily basis since it came out.
The hype around gpt2 was ridiculous. It made me firmly put openai into 'grifters, idiots and probably both' territory.
Turns out they were just grifters as the hilarious mess around Sam Altmans coup/counter coup/coup showed us.
I don't know what your operating definition of "grifter" is but for me, a grifter is not a company that delivers a product which gains a large adoption and mindshare (ChatGPT) and essentially sets the world on fire. (not an opinion on Altman specifically but OpenAI at large)
My definition is someone outright lying that gpt-2 was agi that should be regulated just so they could raise more money for the next round of training.
Who said gpt2 was agi?
I’m not that much of a believer, but what is clear is that “AI” still has a plug incompatible with your regular wall socket, if you get the analogy. It’s too early to draw a circle around adopters count.
We’ll talk counts when my grandma will be able to hey siri / okay google something like local hospital appointment or search for radish prices around her. It already is possible, just not integrated enough.
Coincidentally, I’m working on a tool at my job (unrelated to AI) that enables computer device automation on much higher level than playwright/etc. These two things combined will do miracles, for models good enough to use it.
We're already entering Late Majority stage. Early Majority is like a good chunk of the western population, which already should tell you something - the "technology adoption model" might not make much sense when the total addressable market is literally everyone on the planet, and the tech spreads itself organically with zero marketing effort.
And/or, it's neither hard nor shameful to be True Believers, if what you believe in is plain fact.
IDK. Blockchains have been super hyped. The tech is undeniably cool. But I have yet to see an example of them solving a real problem for anyone who's not a criminal.
In contrast, I've put in very little effort to use AI, but I'm noticing things.
I see high quality AI-generated images in blog posts. They look awesome.
I look over my coworker's shoulder and see vscode predict exactly the CSS properties and values he's looking for.
Another coworker uses AI to generate a working example of FPGA code that compiles and runs on a Xilinx datacenter device.
An AI assistant pops up in Facebook messenger. My girlfriend and I are immediately able to start sending each other high quality, ultra-specific inside joke AI generated memes. This has added real value to my life.
I'm starting to feel FOMO, a bit worried that if I don't go hard on learning this new tool I'm going to be left in the dust. To me at least, AI feels different.
Were regular people using it like ChatGpt?
Yeah up until Mt. Gox exploded and maybe a bit afterwards
After that it started really getting its scammy, investor-only reputation
Every normie I know who has touched crypto ONLY got into it after it gained its scammy investor status. Prior to that it was basically only people like me…dudes running Linux etc, you know the type.
I'll admit blockchains (while they may have potential for the future) don't currently have much use in the real world, but saying the only people it helps are criminals is just that old flawed argument that undermines privacy which in turn only benefits oppressors
I didn't say the only people it helps is criminals. I said that's the only example I've seen in the real world. If you have more I'd be happy to hear about them.
Sending money to family in another country with crap financial infrastructure
I have followed one site that has own AI generator for certain type of content... And after a while it just start to feel samey and soulless... I kinda noticed similar stylistic patterns with text generated I have seen posted... Different styles can be asked, but I wonder how soon the AI output stops feeling worth seeing or reading.
this didn’t really happen the way you want it to. Fortune 50 companies never spent billions of dollars on crypto or NFTs like they are doing for AI. No NASDAQ listed companies got trillion-dollar valuations out of crypto.
There is buy-in happening this time, unlike previous times, because this time actually is different.
I mean, some people see a broad consensus forming and reactively assume everyone else must be stupid (not like ME!). That’s a reflection of your own personal contrarianism.
Instead, try to realize that a broad consensus forming means you actually hold heterodox opinions, and if you think you have a good basis for them that’s fine, but if the foundation for your point that everyone in the world is too stupid to see what’s REALLY going on then maybe your opinions aren’t as reasoned as you think they are. You need to at least understand the values differences that are leading you down the road to different conclusions before you just dismiss the whole thing as “everyone else is just too wrapped into the cult to see straight”.
Bitcoin was actually rebuttable on some easily-explicable grounds as to why nobody really needed it. Why do you think semantic embeddings, semantic indexes/generation, multimodal interfaces, and computationally-tractable optimization/approximation generators are not commercially useful ideas?
I haven't even formed much of an opinion either way, yet. Sure, I have doubt, but that's more of a default than something I reasoned myself into. I'm saying it's just way too early to make statements either way about the future of LLMs and AI that are anything beyond wild guesses. "This time it's different, it's fundamentally transformative and will obviously change the world" is a religious statement when made this early.
Early would have been with GPT-2 writing bad poems. ChatGPT was released 1 year and 7 months ago, so it's still in diapers, but at that age it's already providing value to its users.
Got to imagine that IBM’s spending on their weird blockchain hobby was at least in the hundreds of millions.
And Facebook spent tens of billions of dollars on metaverse stuff, of course.
nvidia did very well out of crypto.
Its because it has to. We're so far behind the 8 ball at this point if it doesn't work we are all screwed.
All of society is so freaking leveraged at this point, something has to give.
can you elaborate on this? in what sense?
I think they're implying AI has too much investment behind it to implode (i.e., it's "too big to fail").
$35T in national debt for one. At some point that house of cards is going to collapse.
I can't see any news out of Sequoia without remember how massively bullish they were on FTX.
they're human and were taken in by con artists. it's a reminder that none of us are infallible.
It's one thing to be taken in by con artists, it's another to publicly boast about your limitless credulity and your complete and utter lack of judgment. Sequoia aren't innocent senile grandmas who got conned by high-pressure scam calls, they're gullible trend-chasing morons who were victims of their own starry-eyed greed.
Imagine looking back on the earth's history from a billion years in the future. There will be a divide around now between a few billion years before when biological life was the most advanced thing going on and after that when AI life was. It's in a different category to the other tech hypes. And I don't really see any possible way that it won't happen(?)
I don’t think anyone doubts that.
The question is when exactly do we get to human level intelligence on all tasks (agi)? Is it going to be GPT5? GPT6? Or has the performance improvements saturated already? It makes a huge difference in terms of your investment and even career decisions whether you expect it to happen next year or 10 years from now.
Fair enough although some people do doubt it. I guess zooming in AI will gradually exceed human abilities in different areas like it's already much better at chess and still rubbish at going down to the shop to get a pint of milk. I'm not sure how the plays into ten year investment and career plans. There's probably a lot of opportunity at the moment for startups to take ChatGPTs abilities and apply them to human tasks involving written language.
This whole AI thing reminds me of the early 90 and people talking about how computers would change the world. Of course they were right but probably not in the ways they expected.
If you go back and look at the overhyped ads back then, you'd see them describing the future that we are currently living, except tainted by some institutions that were too-ingrained; things like "Using the World Wide Web, you will be able to view your checking account balance and make a payment to your utility provider with just a few clicks!" right after someone brings the milk in. i.e., They could predict online banking but not the rise of DoorDash.
Given the accelerated invention/deployment cycles we're in, it's not hard to extrapolate GPT4o to $0 token cost and 0ms latency. Even assuming stagnation in context lengths and cognition, the extreme scope of impact on every computerized industry becomes self-evident.
I think many of the changes actually were predicted. What seems to have been the most unexpected was how it would monetize and where market power would gather.
Online banking is great, and people knew it was coming since the dawn of the internet, but they mostly didn’t predict Stripe.
Don’t assume “widespread deployment of the same”. Think about next-gen models running faster and at a low cost.
They already understand spoken English and can respond in kind.
This is Siri or Alexa on steroids. Just that alone is a “killer app” for everyone with a mobile phone or a home assistant device!
What’s the addressable market for that right now? Five billion customers? Six or seven maybe?
Computer RPG games are about to become completely different. You’ll be able to actually converse with characters.
Etc…
I’m a short-term pessimist but a long-term optimist.
This all reminds me of 3D graphics a in the early 1990s. The nerds were impressed but nobody else thought it was interesting. Now we have Pixar and a computer games industry bigger than Hollywood.
Siri and Alexa are good examples - they turned out to be incredibly expensive to develop and had extremely limited productivity benefits for users, while being almost impossible to monetize.
That's... my point. You're agreeing with me.
Siri and Alexa are bad. Very, very bad.
They pretend to understand spoken English, but they don't, because they're just a huge set of hard-coded rules written out one-at-a-time by enormous numbers of very expensive developers.
This is the 1990s approach to AI: Fuzzy logic, heuristics, solvers, dynamic programming, etc...
That approach has been thoroughly blown out of the water by Transformers, which does all of that and much more with a thousand lines of code that can be banged out in a couple of hours by one guy while talking in a YouTube video: https://www.youtube.com/watch?v=kCc8FmEb1nY
Transformers will revolutionise this entire space, and more that don't even exist yet as a market.
Take for example humanoid robots: Boston Dynamics has had the hardware working well enough for a decade, but not the software. You can't walk up to one of their robots, point at something, and tell the robot to complete a task. It can't understand what it is seeing, and can't understand English instructions. Programming that in with traditional AI methods would take man-millenia of effort, and might never work well enough.
If we could speed up GPT-4o (the one with vision) to just 10x or 50x its current speed, with some light fine-tuning it could control a humanoid robot right now with a level of understanding comparable to C3P0 from Star Wars!
Remember that this is written by Venture Capital investors, and they make high risk, high reward bets.
I don't know the exact numbers, but I guess only maybe 5% of all investments in a given batch make any impact on the total return.
So for a VC, if there's a 10% chance that this whole AI thing will be a financial success, it's chance of success is already twice as high as average, so a pretty good bet.
My rule of thumb is that if someone was super bullish on crypto 3 years ago and is now super bullish on AI then their opinion is probably not worth that much. But if they've been consistently optimistic about AI progress over the last 5-10 years then they probably know what they're talking about.
When you think about the promise (or hype) of crypto/bitcoin/blockchain 10 years ago, in some sense it augured equally, if not more, transformative change/disruption than AI.
Crypto portended drastic and fundamental changes: programmable money, disintermediation, and the decentralization of the very foundations of our society (i.e. money, banking, commerce). Suffice to say that nothing close to this has happened, and probably will never happen.
So I can see how many people are equally skeptical that AI, as the next hyped transformative technology, will achieve anything near the many lofty predictions.
Why is there such an effort to hitch current AI to cryptocurrency? They have nothing in common, not the tech, and not the way people interact with it.
I can't summarize it better than saying that AI hype is deserved, but the excessive euphoria and FOMO are not.
You know I hear ChatGPT is pretty good at summarizing.
They’re a _VC_. If they don’t believe in it, what are they even doing? You can’t really expect them to be particularly objective about it.
Transformers clearly represent a significant advancement in software design, this is undeniable. For example, LLMs are so good at language translation that every approach to machine translation ever conceived of is now obsolete by a very large margin. There are a few other examples and certainly some new ones will emerge, so the tech is good and it's here to stay.
The question of "how do we make money from it" is a much harder to answer. Using every available computer to run quadratic time brute force on everything you can scrape from the internet is an unbounded resource sink that offers little practical return for almost everybody, but leveraging modest and practical use of generative machine learning where it works well will absolutely create some real value.
There's (at least) two meanings for AI: (1) Software systems based on extensions of the LLM paradigm; and (2) Software systems capable of all human cognitive tasks, and then some.
It's not yet clear what (1) has to do with (2). Maybe it turns out that LLMs or similar can do (2). And maybe not.
I can understand being skeptical about the economic value of (1). But the economic value of (2) seems obviously enormous, almost certainly far more than all value created by humanity to date.
The speculation makes sense from a VC's perspective, but perhaps not from the perspective of society at large (i.e. human workers).
From the revenue-generating use cases of LLMs (== AI in the article) that I've seen so far, most seem to be about replacing human mental labor with LLMs.
The replacement of workers with AI-based machines will likely happen in mature industries whose market growth is basically capped. Productivity will stay mostly the same, but the returns will increase dramatically as the human workforce is hollowed out.
To the extent that AI instead empowers some workers to multiply their productivity with the same amount of effort, then it can create more economic value overall, and this may happen in industries with a long growth runway ahead.
On balance, it's not clear to me whether the growth (in productivity and employment) that comes from the latter will be enough to offset the employment losses from the former.
But in either scenario, the VCs investing in AI win, either from efficiency gains, or from accelerating growth in new industries.
Think about all the high volume processes in the enterprise world with a expensive human fallback. Llm are great after the process and before the human to reduce tasks routed to the fallback.
Think about all human involved in producing unstructured document that people have to read like public councils, court of laws, teachers or other evaluators. Llm can be given a relevancy metric and flag content so that those in need of or waiting for a certain information aren't drowned in noise
Llm are unlocking digital transformation in sectors that have been historically resistant to it, and because they are goal driven they do it with minimal programming, just a few good prompts and a data pipeline.
And it's just the tip. They don't get tired, they don't forget nor omit, they are absolutely bonkers to find relevant products and services given a database a need and a set of preferences and they will transform how the next generation will make purchasing decisions, travel decision and all these opinion based choices.
And while llm are likely not the path to agi and they will cap out in capabilities at some point, they are coming down in price super fast, which will propel adoption even for those cases where other options would be more sensible, just because of the sheer convenience of just asking them to do stuff.
Oh, it's really simple, you see if they don't get rewarded handsomely, that proves they didn't focus on delivering true [Scotsman] value. /s
I think they've tapped into something fundamental about knowledge, the mind, and information and this is only the beginning. How many different ways are there to train, wire these up, and integrate with other systems? Then the silicon built for it...I just don't know where the horizon is.