> 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?
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.
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.
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.
- 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.
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.
>I am not consulting ChatGPT over, say, a dietician or doctor
Do you know any doctors, by chance? You have way more faith in experts than I do.
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.
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.
Does it work though, yes it does. There are many human coders who write bad code and life goes.
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.
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!
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 :).
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
> Right now we don’t know the answer to those questions.
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.
> current systems aren’t creating real enterprise value at this moment in time
Yes, they are. They would be creating even more value if not for the copyright and exports uncertainty, which significantly slows enterprise adoption.
It already has at a Fortune 100 company I contract with currently.
> Can it do meaningful work in excel?
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).
> Can it do meaningful PowerPoint work?
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).
> Can it give effective advice on management?
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.
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.
> Can it do meaningful PowerPoint work?
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.
> Can it give effective advice on management?
Yes. Unfortunately can't talk about this except it is mindblowingly good.
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.
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.
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.
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.
Not to mention all the proof-writing that will become simpler with this optimization/searcher 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
I'm not saying AI is useless but it's certainly not the panacea that some say it is.
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.
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.
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]. :-)"
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.
But, tech being impactful doesn’t mean it will create and deliver value for 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.
May we see it?
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 :).
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.
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.
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.
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.
Honestly I'm totally in the AI camp but 6 hours to make a 2048 clone?! And that's a good result? Come on.
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.
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?
After that it started really getting its scammy, investor-only reputation
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.
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.
And/or, it's neither hard nor shameful to be True Believers, if what you believe in is plain fact.
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.
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.
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.
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.
Online banking is great, and people knew it was coming since the dawn of the internet, but they mostly didn’t predict Stripe.
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.
Sometimes the future just gets here before we're ready for it.
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.
I think doubt is OK, at least it is before any particular technology or product has actually proven itself.
> Such strong speculative predictions about the future, with no evidence.
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.
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.
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.
> 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
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.
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.
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 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!
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 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.
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.
All of society is so freaking leveraged at this point, something has to give.
can you elaborate on this? in what sense?
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
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's like everyone is in the same trance and simply assuming and repeating over and over that This Will Change Everything
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?
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..
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.
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.
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
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.
Meta has about 350,000 of these GPUs and a whole bunch of A100s. This means the ability to train 50 GPT-4 scale models every 90 days or 200 such models per year.
This level of overkill suggests to me that the core models will be commoditized to oblivion, making the actual profit margins from AI-centric companies close to 0, especially if Microsoft and Meta keep giving away these models for free.
This is actually terrible for investors, but amazing for builders (ironically).
The real value methinks is actually over the control of proprietary data used for training which is the single most important factor for model output quality. And this is actually as much an issue for copyright lawyers rather than software engineers once the big regulatory hammers start dropping to protect American workers.
Not anywhere close to that.
Those 350k GPUs you talk about aren't linked together. They also definitely aren't all H100s.
To train a GPT-4 scale model you need a single cluster, where all the GPUs are tightly linked together. At the scale of 20k+ GPUs, the price you pay in networking to link those GPUs is basically almost the same as the price of those GPUs themselves. It's really hard and expensive to do.
FB has maybe 2 such clusters, not more than that. And I'm somewhat confident one of those cluster is an A100 cluster.
So they can train maybe 6 GPT-4 every 90 days.
340,000 H100s 600,000 H100 equivalents (perhaps AMD Instinct cards?) On top of the hundreds of thousands of legacy A100s.
And I'm certain the order for B100s will be big. Very big.
Even the philanthropic org Chan-Zuckerberg institute current rocks 1000 H100s, probably none used for inference.
They are going ALL OUT
And then someone else starts giving away shovels for free.
Ah, I see -- it's more like a "level 2 gold rush".
So a level 1 gold rush is: There's some gold in the ground, nobody knows where it is, so loads of people buy random bits of land for the chance to get rich. Most people lose, a handful of people win big. But the retailers buying shovels at wholesale and selling them at a premium make a safe, tidy profit.
But now that so many people know the maxim, "In a gold rush, sell shovels", there's now a level 2 gold rush: A rush to serve the miners rushing to find the gold. So loads of retailers buy loads and loads of shovels and set up shop in various places, hoping the miners will come. Probably some miners will come, and perhaps those retailers will make a profit; but not nearly as much as they expect, because there's guaranteed to be competition. But the company making the shovels and selling them at a premium makes a tidy profit.
So NVIDIA in this story is the manufacturer selling shovels to retailers; and all the companies building out massive GPU clouds are the retailers rushing to serve miners. NVIDIA is guaranteed to make a healthy profit off the GPU cloud rush as long as they play their cards right (and they've always done a pretty decent job of that in the past); but the vast majority of those rushing to build GPU clouds are going to lose their shirts.
And their business model is shovel-fleet logistics and maintenance... :p
Have we been living in the same universe the last 10 years? I don't see this ever happening. Related recent news (literally posted yesterday) https://www.axios.com/2024/07/02/chevron-scotus-biden-cyber-...
Red state blue collar workers got their candidate to pass tariffs. What happens when both blue state white collar workers and red state blue collar workers need to contest with AI. Perhaps not within the next 10 years, but certainly within 20 years!
And if you think 20 years is a long time... 2004 was when Halo 2 came out
But agreed, between the unions with political pull and "AI safety" grifters I suspect there could be some level of regulatory risk, particularly for the megacorps in California. I doubt it will be some national thing in the US absent a major political upheaval. Definitely possible in the EU which will probably just be a price passed on to customers or reduced access, but that's nothing new for them.
But there's bigger fish to fry for American politics and worker obsolescence is not really top of mind for anyone.
The same thinking stopped many legacy tech companies from becoming a “cloud” company ~20 years ago.
Fast forward to today and the margin for cloud compute is still obscene. And they all wish in hindsight they got into the cloud business way sooner than they ultimately did.
What ended up happening was Amazon was better at scale and lockin than everyone else. They gave Netflix a sweet deal and used it as a massive advertisement. It ended up being a rock rolling down a hill and all the competitors except ones with very deep pockets and the ability to cross-subsidize from other businesses (MSFT and Google) got crushed.
> I think it was because we were working on Reels. We always want to have enough capacity to build something that we can't quite see on the horizon yet. ... So let's order enough GPUs to do what we need to do on Reels and ranking content and feed. But let's also double that.
So there's an immense capacity inside Meta, but the _whole_ fleet isn't available for LLM training.
[0]: https://www.dwarkeshpatel.com/p/mark-zuckerberg?open=false#§...
What IS a huge problem is the almost complete lack of systematically acquired quantitative data on human health (and diseases) for a very large number (1 million subjects) of diverse humans WITH multiple deep-tissue biopsies (yes, essentially impossible) that srr suitable for multiomics at many ages/stages and across many environments. (Note, we can do this using mice.)
Some specific examples/questions to drive this point home: What is the largest study of mRNA expression in humans? ANSWER: The small but very expensive NUH GTEx study (n max of about 1000 Americans). This study acquired postmortem biopsies for just over 50 tissues. And what is the largest study of protein expression in humans across tissues? Oh sorry, this has never been done although we know proteins are the work-horses of life. What about lipids, metabolites, metagenomics, epigenomics? Sorry again, there is no systematically acquired data at all.
What we have instead is a very large cottage-industry of lab-level studies that are structurally incoherent.
Some brag about the massive biomedical data we have, but it is truly a ghost and most real data evaporates with a few years.
Here is my rant on fundamental data design flaws and fundamental data integration flaws in biomedical research:
Herding Cats: The Sociology of Data Integration https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2751652/
> Think of how cancer could have been cured a decade ago if information was allowed to flow freely from the 50's forward
might be a bit fanciful? Unless you're referring to something particular I'm unaware of.
The people best equipped and trained to deliver a cure for cancer (and then some, since it tends not to be particularly field-restricred) do have access.
I think the loss is more likely in engineering (to the publication's science), cheaper methods, more reliably manufacturable versions of lab prototypes, etc.
I doubt there are many people capable of cancer research breakthroughs who don't have access to cancer research, personally.
(And to be clear: I'm not capable of it.)
The schools I’ve worked with have access to everything I’ve needed. They didn’t advertise it but it’s also free for students.
2) There may be a few researchers who don't have unfettered access. Perhaps they paid $40 for a copy of a paper. Given the high cost of other parts of research labs, I find it hard to believe that any real possibility of curing cancer was halted because someone had to pay $40.
3) It's possible to imagine the opposite being the case. Perhaps someone had a key insight in a clever paper and decided to distribute it for free out of some info-anarchistic impulses. There it would sit in some FTP directory uncrated, unindexed and uncared for. Perhaps the right eyes would find it. Perhaps they wouldn't. Perhaps the cancer researcher would be able to handle all of the LaTeX and FTP chores without slowing down research. Perhaps they would be distracted by sys admin headaches and never make a crucial follow up discovery.
The copyrighted journal system provides curation and organization. Is it wonderful? Nah. Is it better than some ad hoc collection of FTP directories? Yes!
Your opinion may be that this scenario would never happen. In my opinion, this is more likely than your vision.
[0] https://en.wikipedia.org/wiki/Slavery_Abolition_Act_1833
The precisions and mantissa/exponent ratios you want for inference are just different to a mixed-precision, fault tolerant, model and data parallel pipeline.
Hopper is for training mega-huge attention decoders: TF32, bfloat16, hot paths to the SRAM end of the cache hierarchy with cache coherency semantics that you can reason about. Parity gear for fault tolerance, it’s just a different game.
If there's dedicated inferencing silicon (like say the thing created by Groq), all those GPUs will be power sucking liabilities, and then the REAL singularity superintelligence level training can begin.
Maybe. But we've barely scratched the surface of being more economical with data.
I remember back in the old days, there was lots of work on eg dropout and data augmentation etc. We haven't seen too much of that with the like of ChatGPT yet.
I'm also curious to see what the future of multimodal models holds: you can create almost arbitrarily amounts of extra data by pointing a webcam at the world, especially when combined with a robot, or letting your models also play StarCraft or Diplomacy against each other.
What it actually means is that they are training next gen models that are 50X larger.
And, considering MS and OpenAI are planning to build a $100 billion AI training computer, these 350K GPUs is just a tiny portion of what they are planning.
This isn't an overkill. This is the current plan: throw as much compute as possible and hope intelligence scales with compute.
Could you expand on this? Who are "the builders" here? You mean the model developers? I don't see how this situation can be "amazing" for the builders - developers will just get a wage out of their work.
I agree though that the returns on hardware rapidly diminish.
The US Supreme Court seems determined to make sure that big regulatory hammers are not going to be dropping, from what I can tell.
When NAR settled the price collusion charge? Thus cartel or not, times do change.
A good real estate agent can guide people through this process while advising them on selling at the right price while avoiding the most stress often during an extremely difficult time in their life, such as going through divorce of breakup. They of course also help keep buyers interested while the seller is making up their mind about the correct offer to take.
I find your comment ignorant in so many ways. Maybe have some respect?
https://hai.stanford.edu/news/ai-trial-legal-models-hallucin...
The returns are going to chip-makers and employers, including single founder startups, who don't have to hire a lot of people, and additionally get productivity they never thought possible. A surgeon I know uses AI every day - to translate, to explain, to figure out problems, to write. They wouldn't have paid someone to do that, but now they get that output in seconds. This is a time to solve all kinds of problems we didn't think possible - because AI has made the enterprising among us instantly smarter.
All the fodder about AGI being a next step is smoke and mirrors - for everyone using OpenAI knows they don't need any more niche tools as their one $20 subscription is doing more for them every day. AGI is here. Experts can correct AI generated mistakes, but those are getting less and less too. The real benchmark is: Name how many people you know who can out-do ChatGPT on a question. You won't bother to check LinkedIn for that.
The gains are aggregating towards Chips, Clouds and Entrepreneurs. The VCs, since A16Z's original AI blog post (all expense, little return, echoed this Sequoia post but did it but years ago), know they are not needed as much anymore. Fewer VCs will beat the market when founders can grow startups without raising too much money (they don't need to hire as many people). Hiring needs lead to PR waves which require VC funding. Valuation is not a big deal for founders making money either, so they may not even disclose how successful their companies are. Bragging about your gains only invites competition. So other than ponzi-type ventures where you need to attract the dinner to serve dinner, you won't hear much about the good ones.
A different era indeed. The tech giants are in for a lot of change as well. Those who have distribution may try to push their models to the masses to be the point of reference, but that can get expensive, especially for those who don't charge. AI will help improve AI performance as well and that means cheaper better performance with time.
What's most needed in this era are people who know what the world needs that hasn't been invented yet. They need to be inventing and monetizing it. Little stopping you now.
I am also using this to solve problems that are usually delegated to junior developers. I get faster results (that I would have to fix up anyways) for far less effort.
Not just that AI is a great tool that will over time have a significant impact on society. It’s the breathless hype e.g. we have AGI today, society will be instantly smarter because of it and every tool and employee will be impacted. The FOMO i.e. you must jump on board now or be left behind. And the complete lack of any data or evidence.
Are the gains aggregating to entrepreneurs? It seems like every just a matter of time before the function of the big models overtakes any entrepreneurial idea other than hardware and the models themselves.
In software the UX is the big burden AI will overcome. Not having to deal with all the typing and clicking frees up more tasks and energy for deciding what needs to be done. Brain energy.
Distraction industries, like sugar, become obviously tart when oversaturated and AI is quickly pushing that boundary. Once people realize they’ve been served a pacifier to the brain in every free minute, they will start thinking more and consuming less.
So now AI frees more human brain energy towards what is actually needed - what problems need solved for humans, not for overloaded interfaces or zombified content consumers.
Examples: Trustworthy, efficient and safe transport for kids to and from enrichment activities. Education of others on tasks that could earn them a living. A better healthcare experience, because 60% of the admin paperwork is AI automated and health workers can talk to patients instead of typing. Organizing people to do more things together to clear the social-media-generated loneliness epidemic. Making new, interactive experiences people would pay to do, especially with friends. People ask for more things to look forward to. Not more things to type. AI is a new hands-free take on UX and UI.
>The Coffee Test (Wozniak)
>A machine is required to enter an average American home and figure out how to make coffee: find the coffee machine, find the coffee, add water, find a mug, and brew the coffee by pushing the proper buttons.
That is a step yet to come.
The holy grail of AI is still in future. Where it can interact with software tools like we do. Competent AI Agents will be a huge productivity unlock.
The people expressing themselves with image AI would never have gone to fiverr or upwork to begin with.
And only some of the existing clientele would change their behaviors too.
Additionally, I know artists that always wanted to express themselves differently than what they became known for, and actually create and sell AI generated work to their clientbase now.
I put this as equivalent to investing in Sun Microsystems and Netscape in the late 90s. We knew the internet was going to change the world, and we were right, but we were completely wrong as to how, and where the money would flow.
Sun Microsystems sold to Oracle for $7B, and Netscape was acquired by AOL for $10B.
Cisco too.
Highly debatable.
When we look back during the internet and mobile waves it is overwhelmingly the companies that came in after the hype cycle had died that have been enduring.
"Several studies have shown that pioneers have long-lived market share advantages and are likely to be market leaders in their product categories. However, that research has potential limitations: the reliance on a few established databases, the exclusion of nonsurvivors, and the use of single-informant self-reports for data collection. The authors of this study use an alternate method, historical analysis, to avoid these limitations. Approximately 500 brands in 50 product categories are analyzed. The results show that almost half of market pioneers fail and their mean market share is much lower than that found in other studies. Also, early market leaders have much greater long-term success and enter an average of 13 years after pioneers."
PDF available here:
https://people.duke.edu/~moorman/Marketing-Strategy-Seminar-...
> Founders and company builders will continue to build in AI—and they will be more likely to succeed, because they will benefit both from lower costs and from learnings accrued during this period of experimentation
This still lines up with the 2nd wave benefiting more. The first movers helped established the large scale AI hardware industry, got a bunch of smart kids trained on how to make AI, a bunch of people will fail and learn, etc and this experimentation stage sets the groundwork for OpenAI 2.0.
We could very well just in the Altavista vs Yahoo days of AI and an upstart takes over in 5yrs.
Microsoft Office: wasn't close to the first office editing suite
Google: Wasn't close to the first search engine
Facebook: Wasn't close to the first social media website
Apple: ~~First "smart phone"~~ but not the first personal computer. Comments reminded me that it wasn't the first smartphone
Netflix: Wasn't close to the first video rental service.
Amazon: Wasn't close to the first web store
None of the big five were first in their dominate categories. They were first to offer some gimmick (i.e., google was fast, netflix was by mail, no late fees), but not first categorically.
Though they certainly did benefit from learnings of those that came before them.
Was it the first smartphone? I would call phones like the Palm Treo and later BlackBerries smartphones. There were even apps, but everything was lot more locked down and a lot more expensive.
Just to quibble with this - that was not even close to the reason Google got popular. It was because Google was much, much better at finding what you actually wanted. It was just a far better product.
You can debate why this is exactly, Joel Spolsky pointed out many years ago that it was because Google got that what matters to users most isn't "finding all pages related to X" but rather "ranking" those pages, a take I agree with.
"key differentiator" and not necessarily easy to pull off or pay for
So why aren't there more entrants in the CPU cloud area? The technology is a commodity. Google and Amazon don't make CPUs.
Because the market is saturated with players.
AWS, Azure, GCP, Alibaba, IBM Cloud, Digital Ocean, Tencent, Oracle Cloud, Huawei Cloud, that Dell/VMware thing, Linode/Akamai, HP, Scaleway, Vultr, GoDaddy, OVH, Hetzner, etc.
Source: I run a cloud company and have plenty of friends in the space.
I'm reminded of Bitcoin/crpyto, which in its early history was all operated on GPUs. And, then, almost overnight, the whole thing was run on ASICs.
Is there an intrinsic reason something similar couldn't happen with LLMs? If so, the idea of a bubble seems even more concerning.
I found a short discussion[2] you may find useful.
[2]: https://www.lesswrong.com/posts/qhpB9NjcCHjdNDsMG/new-fast-t...
Only if we can increase the efficiency of LLMs by 2-3 orders of magnitude, there are only some in lab examples of this and nothing really being publicly shown.
Even then the models are still going to require rather large amounts of memory, and any performance increases that could boost model efficiency would very likely increase performance on GPU hardware to the point we could get continuous learning models from multimodal input like video data and other sensors.
While people may say something is a Transformer that's more of a general description. It's not a specific algorithm; there are countless transformers and people are making progress on finding new ones.
Bitcoin runs a specific algorithm that never changes. That's for an ASIC. AI/ML runs a large class of models. GPUs are already finely tuned for his case.
This is one of the first time in the tech industry where the value was fully reaped by the hardware itself and not by the differentiated software that ran on top of it
Silicon is currently the most advanced tech humans have ever made and those GPUs are on the cutting edge of that
The biggest takeaway from this piece is the stark realism of this article (maybe a bit too bearish, imo) compared to the usual Sequoia VC-speak. Maybe FTX did teach them something, after all.
VCs did get burned by speculative investing in the 2019-21 period but FTX wasn’t like the others. At the time of its collapse, FTX was profitable and had $1+ bn in revenue, its doom had nothing to do with product market fit, revenue, margins, etc.
Quibi might be a more relevant example of a learning opportunity.
Comparing this article to the (now deleted) SBF profile is night and day.
> Maybe FTX did teach them something
The new tone is all about business fundamentals and the FTX collapse had literally nothing to do with business fundamentals. If this is the lesson they learned they learned the wrong lesson.
For the case where a company is using their own AI for their own cost reduction and productivity improvements, they can keep doing that but not offer to another party.
If they offer to another party, and that party is having benefits (like you have said), the price should be such that a part of the consumer benefit is shared with the producer resulting in benefits for the producer.
The real challenge here is because of price wars, i.e., too much competition already with producers willing to take a hit on profitability in anticipation that they will be able to do so later after creating a moat above and beyond competitors. Or they think that it will strenghen their overall bigger offering by adding an otherwise lossy feature.
In a nutshell, even if there's a lot of value for the consumers, it must result in a win-win for a new product to be sustainable in the market.
Well if there's value to you then how much did you pay for it and would it realistically cover operating cost once VC cash dries up? That's the only question.
But other than FOMO why would someone buy better chips when they don't actually know what to do with their old ones?
1. That all datacenter GPUs being purchased are feeding AI. You might be able to argue that some are or a lot are, but you don't know how many just looking at Nvidia sales numbers. I know of at least two projects deploying rows of cabinets in datacenters full of GPUs for non-AI workloads.
2. The assumption that pay-for-an-API is the only AI business model. What we now call "AI" has been driving Google's search and ad businesses for nearly a decade, sooo AI is already doing $300B/yr in revenue? There is no way for this guy to quantify how AI is solving problems that aren't SaaS.
David Chan, if you are reading this feel free to email me if you want a fact check for what will surely be the third installment in the series.
So while I initially thought customer-facing roles would be front and center in the "AI revolution." Today, I tend to think they'll be bringing up the rear, with entertainment/smut applications at the forefront along with a few unexpected applications where LLMs operate behind the scenes.
But in which sector will these extemely important companies be active? Adtech? Knowledge management / productivity tools? Some completely new category?
What is an undeniable fact is the drastic commodization of the hardware / software stack for certain classes of algorithms. How is this technological development going to be absorbed and internalized by the economy feels still rather uncertain.
I dont think anyone knows the answer to that question.
$600 billion is $434 per person, $36 per month per person, 1.2% percent of GDP.
If 75% of the spending goes to increasing productivity, I could see it. Get rid of $450 billion in labor costs (12 - 15 million work years). Cut worked hours in call centers, customer service, many services, menial programming jobs, ...
But I don't see it happening fast enough to pay for current investments.
What if these AI investment were only to protect or strengthen their current business? AI in Windows, macOS, Adobe, iPhone, Facebook, Instagram may not bring in any additional revenue. But it add additional value to their current product line, making competition harder, further hardening their moat.
Nvidia or Jensen is also smart to play the national security card. Does the European want their model to be all US based. Are the answer culturally correct? Just like how every single country invested in their own Telecom or Internet infrastructure, if this pitch were even half as successful, do these numbers we are looking at even matter when it is spread out across G7 or G20?
While I believe we are still far, or at least 10+ years away from AGI, the current form of AI still has a lot of improvement incoming and are already bringing in real world benefits and value to a lot users. The adoption curve will accelerate once it is integrated into Windows, Office and Mac. So even if we are in a bubble, I still think we are very early in the curve before it burst.
So we have this bearish piece and the previous bearish Goldman Sachs piece. While I agree with their analysis in this case, there is a lingering doubt that some banks might just want to tank Nvidia a little in order to go long. Or something like that.
#1 Just like the intranet there are a lot of productivity gains that firms like Tesla, Meta, Google and Amazon can gain internally by optimizing their own workflows. That in itself should justify the investment. Granted some of these optimisations will use their own chips instead of Nvidia, but Nvidia will get a lion-share of this.
#2. Then there are other verticals - pharma, oil and gas, logistics who can optimize their internal workflows to gain productivity. It just helps them improve their margins. No end user benefit may be realized and that’s fine.
#3. Nation states are buying GPUs too. Ex: Falcon2 was trained on a cluster owned by a middle eastern country. Nation states see something larger at stake here than just releasing an app. This does not have to even be a profitable endeavour.
Modern web is full of examples of platforms that really don't make money...
Sell! Sell! Sell now before it's too late!
Most AI startups aren't building massive data centers so they're unaffected. Most money isn't spent on compute in most startups. Only a few companies spend big.
It's obviously a terrible idea to invest massive amounts into compute when Nvidia's profit margins are so astronomical if you need ROI in the long term. The massive corporations won't get their money back for these investments; but they don't have to.
Investors first need to ask what that ratio might look like in 10 years. 10-to-1? 100-to-1? Inference-to-Training
Assuming for each NVDA training GPU sold there are 100 open source / commodity GPUs doing inference, who owns and supplies those data centers and hardware?
The author of this blog makes a great argument that there is a risk of AI investments not paying off because if a revenue gap: The author argues that the gap between the revenue expectations implied by the AI infrastructure build-out and actual revenue growth in the AI ecosystem has increased from $200B to $600B. This is due to factors such as the subsiding of the GPU supply shortage, growing GPU stockpiles, and the dominance of OpenAI in AI revenue. The author also notes that the $125B hole in AI revenue has now become a $500B hole.
However, my experience with previous AI winters is not relevant here because now is the first time in history that there is a possibility of what we used to call “real AI” and now call AGI. No investor wants to miss out completely on the creation of near limitless wealth.
Assuming startups like Etched (with its recent massive funding) could shrink CapEx quite a bit (and make it not such a large revenue shortfall)
Just two words to describe all the idiocy of the current wave of AI offerings.
> 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. We are living through what has the potential to be a generation-defining technology wave.
No. It's crap no matter how much money you throw at it. It will end in tears, because there is no standard, no operating system, no protocol, only a handful of APIs controlled by a couple of companies. They are not building networks, but hubs with a single point of failure--the API provider. The internet is such a world-changing force because it is built on top of TCP/IP, which allows the rest of the internet to survive even if a part of it goes down. When an AI API provider shuts down, all those bullshitters repackaging LLMs will be left holding the bag, or rather their investors will be. In a way, the coming AI bubble burst is going to be a self-fulfilling prophecy--AI will make its creators redundant.
> But we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world. That delusion says that we’re all going to get rich quick, because AGI is coming tomorrow, and we all need to stockpile the only valuable resource, which is GPUs.
No, the delusion is that Gen AI is good for anything. It is not.
Soon, the $600B question is going to be, "where's the money gone?"
What do you get if you multiply six by seven
