According to Jensen it takes about 8000 H100s running for 90 days to train a 1.8 Trillion param MoE GPT-4 scale model.
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.
> This means the ability to train 50 GPT-4 scale models every 90 days or 200 such models per year.
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.
I honestly don't think we've seen the end of AR/VR yet. The tech continues to improve year over year. There are rumors the prototype Zuck plans to show at Meta Connect this year are mindblowing
Better VR tech won't make people buy VR. You could literally offer them a Star Trek holodeck and they still wouldn't buy in. People don't buy it because they don't see the point.
This was even true in Star Trek. People could do literally anything on a holodeck and the writers still had them going to Risa for a holiday.
There is no chance of VR going mainstream until someone solves the fundamental human problem of people preferring to do things in real life.
> This was even true in Star Trek. People could do literally anything on a holodeck and the writers still had them going to Risa for a holiday.
If anything, that was a failure of imagination on writers' part, somewhat rectified over time and subsequent shows. Even in the core shows (TNG, DS9, VOY), we've seen the holodeck used for recreation, dating, study, simulation, brainstorming, physical training, hand-to-hand combat training, marksmanship training, gaming out scenarios for dangerous missions, extreme sports, engineering, accident investigation, crime scene reconstruction, and a bunch of other things. Still, the show was about boldly going where no one has gone before - not about boldly staying glued to a virtual reality display - so this affected what was or wasn't shown.
Plus, it's not either/or. People went to Risa to have real sex (er, jamaharon) with real people, and lots of (both it and them). This wasn't shown on-screen, just heavily implied, as this is where Roddenberry's vision of liberated humanity clashed with what could be shown on daytime TV. Holo-sex was a thing too, but it was shown to be treated more like pornography today - people do it, don't talk much about it, but if you try to do it too often and/or with facsimile of people you know, everyone gets seriously creeped out.
In TNG we see Barkley get addicted to the holodeck and use it to play out fantasies with female members of the crew. Through the episode we end up learning that Barkley escaped to the holodeck because he was having problems and not being fulfilled in his real life.
There was a similar episode of DS9 where Nog gets addicted to the holodeck due to war trauma.
The central take of the show is that real life is better for these people in this future communist space utopia and the only reason why you'd go to the holodeck is light entertainment, physical training, or if there's something wrong with your life that needs fixed.
so much human potential, natural resources, and anxiety wasted on obsessive pursuit of diddling a few special nerve endings, heaped in a mountain of self-serving social pecking order mythology and ritualistic mystery.
Once we can produce offspring in sci-fi vats, then we can remove the then unnecessary organs from our DNA and not have those worries. We can be just like human ants where the queen is now vats and we just work and maybe think a little.
Totally agreed. It's like the hype around "social media" or "streaming services" or "video games". There's no chance of any of them going mainstream because of the fundamental human problem of people preferring to do things in real life.
VR requires too much setup. I have a PS4, bought a used PSVR set and realized I needed a camera that I did not have. Realized instead of buying a camera, I could upgrade to a ps5 and buy the new headset that did not require a camera, bc I prefer not to have my living room look like a lab. Then there is the social aspect of it.
You can't interact with people around you the same way you do if you play with, say, a console controller. VR is an all encompassing activity that you have to dedicated time for, instead of having it casually just exist around you. Then we have the cost. Only some people can have it, so it will be a lonely activity most of the time when it could be so much more.
I can afford it, but every time I am in front of a new set, I consider my life with it and say "maybe next time". Finally, I have not really explored them, but I have a feeling the experience is limited by the content that exists.
I dream of a VR experience where suddenly all content I currently enjoy on flat screens will automagically be VRified. But I am pretty sure that will not be the case. Only a very limited collection will be VR native.
But I want it all to be, or almost all, before I go all in.
> VR requires too much setup. I have a PS4, bought a used PSVR set and realized I needed a camera that I did not have. Realized instead of buying a camera, I could upgrade to a ps5 and buy the new headset that did not require a camera, bc I prefer not to have my living room look like a lab. Then there is the social aspect of it.
I bought the somewhat dated Quest 2 sometime in the last year, because I could get it for a good price. There's a mobile app I think I used for setup, you can also connect to the PC for Oculus Link, SteamVR, Virtual Desktop or any other number of OpenXR apps or games, but as for the device itself... there was basically nothing aside from logging in and downloading what I want from the store, if I wanted to run things directly on the headset. The controllers and tracking just works, you define the area you want to get warned about getting close to the borders of by just drawing in the room around you.
Actually, the only problems I've had have been in a PCVR use case after I got an Intel Arc - Oculus Link and SteamVR both don't support it natively (an allowlist in the case of the former and support only for NVENC I think in the case of the latter), whereas Virtual Desktop worked with AV1 and Intel QSV out of the box, while also allowing me to launch SteamVR through it.
There are warts and all (especially software like Immersed removing support for physical monitors, what were they thinking), but in general the hardware and everything around it, even hand tracking, are pretty well streamlined, surprisingly so.
> VR is an all encompassing activity that you have to dedicated time for, instead of having it casually just exist around you.
This kind of killed it for me, to be honest. There's more friction than just launching a game on the PC directly (in the case of PCVR: putting the headset on, connecting to the PC, then launching it on the PC, finally accessing it on the headset) in addition to needing to sometimes use the keyboard being especially annoying, since the on screen keyboard is just more annoying to use and having to find your regular keyboard taking a step or two, if you're standing instead of sitting while playing.
That said, VR in general still feels cool, even if it's a bit early.
The appropriate response to VR is that we all get a VR/storage/etc room in addition to the existing paradigms of bedroom, living room, kitchen, etc. At the high end we've grown houses to the point that in order to remain boxes they demanded interior rooms without windows, and so far we have varied between refusing to build these rooms because "natural light" and outright banning these rooms for safety reasons, creating sprawling complicated floorplans instead with lots of surface area per volume.
It would be a bit better suited to a civilization that wasn't undergoing a catastrophic urban housing shortage crisis with demographic & economic effects for upcoming generations that are comparable to a world war or the Black Death. We are building huge exurban houses which nonetheless do not have VR-appropriate rooms, and tiny 1-bedroom apartments, and not much else. https://www.youtube.com/watch?v=4ZxzBcxB7Zc
The question is whether this is a chicken/egg problem that prevents us from launching next-generation VR plays.
I think the key there (the “killer app” as it were) is shared experiences. I love co-op gaming and keep in touch with faraway friends by playing those games while Discording. It would be a game-changer (literally and figuratively) if we could game or watch something in the same AR or VR space, with some kind of persona or avatar representing ourselves, and spatially reflecting our audio/voice.
We’ve joked multiple times that whenever the “co-op Skyrim VR” of gaming comes out, we will never be heard from again lol.
Apple is SO CLOSE to this with its Vision Pro hardware, and yet so far… (no “co-op space” implementation, too expensive, too heavy on face)
Imagine seeing a live soccer match in 3D from incredible camera angles like just above the goals, but your buddy who is 3000 miles away is actually also sitting right next to you in that space, and you can see and hear each other…
> There is no chance of VR going mainstream until someone solves the fundamental human problem of people preferring to do things in real life.
Three counterpoints: Online gaming, social media, smartphones. All of these favor "virtual" over "real life", and have become massively popular over the last decades. Especially among the young, so the trend is likely to continue.
Think you're off base here and the issue is comfortability. People spend all day on their computers and phones, VR just needs to make some breakthroughs in comfort (maybe built-in fans? Literally include ginger tablets with the headset?) to get people over the initial nausea hump. This plus higher resolution for AR purposes will do a ton.
Now, there may also be a physical laziness factor to overcome, but there are enough people that enjoy moving their bodies to really explode the industry even if all the lazy folks stay 2D.
Sounds like more unsubstantiated hype from a company desperate to sell a product that was very expensive to build. I guess we'll see, but I'm not optimistic for them.
Ok, I might have misread some rumored ballpark figures. And most of the GPUs will be used for inference rather than training. Still 6 GPT-4's every 90 days is pretty amazing.
It's like someone thinking that they are SOOOO smart, they are going to get rich selling shovels in the gold rush. So they overpay for the land, they overpay for the factory, they overpay for their sales staff.
And then someone else starts giving away shovels for free.
> 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/or exploiting the legal infrastructure around intellectual property rights to make sure only hobbyists and geologists can use the shovels without paying through the nose or getting sued into oblivion.
If your company grows to 700 million monthly active users, then most probably you can make your own AI department and train your own models. I guess people's aspirations are very high in this space, but let's be realistic.
I think people wildly underestimate how protectionist people - particularly educated software engineers and PhDs will get once an AI model directly impacts their source of wealth.
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
> I think people wildly underestimate how protectionist people - particularly educated software engineers and PhDs will get once an AI model directly impacts their source of wealth.
I don't know what power you imagine SWEs and PhDs posses, but the last time their employers flexed their power by firing them in droves (despite record profits); the employees sure seemed powerless, and society shrugged it off and/or expressed barely-concealed schadenfreude.
They were sued for collusion and the lawyers got a massive payout and the employees got a fraction of lost wages. I was one of them. (Employees not lawyers.)
It's not going to stop it though even if they try though. You can't stop technical progress like this any more than you can stop piracy.
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.
I keep seeing people say you can’t stop progress (social, technical, etc.) but has this really been tested? There seems to be a lot of political upheaval at least being threatened on the near future, and depending on the forces that come into power I imagine they may be willing to do a lot to protect that power.
Tucker Carlson at one point said if FSD was going to take away trucking jobs we should stop that with regulation.
> making the actual profit margins from AI-centric companies close to 0
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.
That's not the way I remember the cloud transition at all. My company adopted an internal cloud. VMWare had some great quarters. Openstack got really big for a while and everyone was trying to hire for it. All the hosting companies started cloud offerings.
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.
It still blows my mind that Microsoft is the most valuable company in the planet because of the cloud and Balmers long term vision. I thought they would have gone the way of IBM.
It’s not just the software, it’s the hardware too. Too many companies got good at speeding up VM deployments but ignored theory of constraints and still had a 4-6 month hardware procurement process that gave everyone and their dog veto power.
And then you come to companies that managed to streamline both and ran out of floor space in their data center because they had to hold onto assets for 3-5 years. At one previous employer, the smallest orderable unit of compute was a 44U rack. They eventually filled the primary data center and then it took them 2 years to Tetris their way out of it.
You're not switching cloud providers. Amazon's not going to suddenly decide to jack up rates for EC2 instances on you. So the extra complexity just isn't worth it.
There is a hypothetical "but what if we honestly actually really really do", but that's such a waste of engineering time when there are so many other problems to be solved that it's implausible. The only time multi-cloud makes sense is when you have to meet customers where they're at, and have resources in whichever cloud your customers are using. Or if you're running arbitrage between the clouds and are reselling compute.
Not really. What happens when you run a cloud on top of your cloud is that you don’t get to use any of their differentiating features and that winds up costing you money. Plus you have to pay for your own control plane when that’s already baked into the cloud provider’s charge model.
per Zuckerberg[0], ~half of their H100s were for Reels content recommendation:
> 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.
In my opinion Elsevier and others charging for access to academic publications has held back the advancement of humanity to a lower exponential acceleration into the future at a considerable factor. Think of how cancer could have been cured a decade ago if information was allowed to flow freely from the 50's forward - if anyone could have read scientific publications for free. I have no respect for people that want to protect the moat around information that could be used to advance humanity.
Have to disagree. Almost all researchers have essentially unfettered access to all of biomedical literature. Access to papers is therefore a tertiary annoyance wrt progress in science and the cures for cancers.
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:
I also think the bottleneck isn't access to the papers today and data access and silos are more important.
But I also think the GP's claim and yours are not incompatible. I wonder how much survivorship bias this has since it only considers those that are able to do research, and not those that would have but ended up doing continuing with another STEM job. We could be asking the counterfactual that I think the GP is implying: would more people have been interested in becoming cancer researchers if publications were open?
We can sort of see the effect because we have scihub now, which basically unlocks journal access for those that are comfortable with it, and I consider it plausibly having a significant effect for the population that have a research background without an academic affiliation. I've met a few biotech startup founders that switched from tech to bio and did self study+scihub outside of the university. The impetus for change I've heard a few times is a loved one got X disease, and I studied it, quit my less impactful tech job to work on bio stuff.
As much as I'd love open access to academic publications and don't think the current model is great:
> 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.
I’ll add that even if the papers we all wanted were more freely accesible, the replication and completeness of their described methods would be another source of slowdown.
Main problem is still just getting good quantitative data and metadata. Most biomedical researchers are motivated to “tell stories”. Few of us care about generating huge mineable data sets.
All of the engineering companies I’ve worked for have not paid for IEEE or any journals. I have to go to the library and maintain membership for IEEE myself then request reimbursement.
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.
1) First, most researchers at universities or other institutions have always had unfettered access thanks to a site license. It would be pretty hard to find a real example of a university researcher who couldn't see something.
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.
You have to not latch on to causes such as "advance humanity" and then justify making people do work for free. We decided a while ago[0] that making people work for free was a bad thing. There is high demand for curing cancer. Every company that tries it will hire scientists and lab techs and large laboratories, and have subscriptions to journals. Do you think all of those people should work for free, in the cause of advancing humanity?
But anyone who needs to see these publications can reach them through libraries. One of the reasons why Elsevier can charge so much is that their customers have been institutions.
It’s been a very long time since I had any inside baseball, but I very much doubt that Hopper gear is in the hot inference path.
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.
True that, but I think in a very short amount of time, using dedicated general purpose GPUs just for inferencing is going to be mega overkill.
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.
Etched is another dedicated inference hardware company that recently announced their product. It only works for transformer based models, but is ~20x faster than a H100
> 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.
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.
There are more kinds of AI-centric companies than just foundation models. Making that equivalency is akin to equating internet companies during the dotcom bubble with just websites like pets.com. Now one semi-skilled person in a couple days can make websites that entire teams back then would have taken months to build, but that doesn't mean google.com and thefacebook.com are easily commodotized or bad businesses just because they're websites.
> this means the ability to train 50 GPT-4 scale models every 90 days or 200 such models per year.
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.
Propriety data is not necessary for training intelligence. Wikipedia, pubmed, arxiv, Reddit and github are probably sufficient. And babies don’t even use that.
I agree though that the returns on hardware rapidly diminish.
Llama isn't even in the same stratosphere as the big models when it comes to coding, logic, and other interesting tasks that I think are commercially viable.
Nothing will put lawyers or doctors out of work. They are powerful cartels that can easily protect themselves. Realtors are already irrelevant technologically but they have a huge entrenched social and legal system to make it impractical to compete.
My friend is a real estate agent, they play a major part in the psychology of buyers and sellers. Selling your dead parents home that you grew up in (for example) isn't something everyone just signs up to some website and does using a credit card without a second thought.
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?
Are you not just explaining "a huge entrenched social system" as OP said?
It takes a long time for cultures to shift and for people to start to trust information systems to entirely replace high touch stuff like that. And at some level there will always be some white glove service on top for special cases.
How is hiring a professional to help you sell a property a "huge entrenched social system" sorry ? No one is forced to hire a real estate agent. I bought my house through private sale.
Lexis+ AI and Ask Practical Law AI systems produced incorrect information more than 17% of the time, while Westlaw’s AI-Assisted Research hallucinated more than 34% of the time:
The failures are different from my experience in this.
Human lawyers fail by not being very zealous and most of them being very average, not having enough time to spend on any filings, and not having sufficient research skills. So really, depth-of-knowledge and talent. They generally won't get things wrong per se, but just won't find a good answer.
AI gets it wrong by just making up whole cases that it wishes existed to match the arguments it came up with, or that you are hinting that you want, perhaps subconsciously. AI just wants to "please you" and creates something to fit. Its depth-of-knowledge is unreal, its "talent" is unreal, but it has to be checked over.
It's the same arguments with AI computer code. I had AI create some amazing functions last night but it kept hallucinating the name of a method call that didn't exist. Luckily with code it's more obvious to spot an error like that because it simply won't compile, and in this case I got luckier than usual, in that the correct function did exist under another name.
True, they're similar... But what's also similar is that people make the mistake of focusing on differences in failure rates while glossing over failure modes.
Human imperfections are a family of failure-modes which have a gajillion years of experience in detecting, analyzing, preventing, and repairing. Quirks in ML models... not so much.
A quick thought-experiment to illustrate the difference: Imagine there's a self-driving car that is exactly half as likely to cause death or injury than a human driver. That's a good failure rate. The twist is that its major failure mode is totally alien, where units attempt to inexplicably chase-murder random pedestrians. It would be difficult to get people to accept that tradeoff.
No, people have the correct intuition that human errors at human speeds are very different in nature from human rate errors at machine speeds.
It's one thing if a human makes a wrong financial decision or a wrong driving decision, it's another thing if a model distributed to ten million computers in the world makes that decision five million times in one second before you can notice it's happening.
It's why if your coworker makes a weird noise you ask what's wrong, if the industrial furnace you stand next to makes a weird noise you take a few steps back.
The infinitely expanding AI-generated metaverse isn't going to render itself, at least in the case of meta I think that might be one of the only pieces missing.
I think this is the correct take. My understanding of the article is that huge investments in hardware, mostly to NVIDIA, and spending by major tech companies is currently defining the market, even if we include OpenAI, Anthropic, etc. It is FAANG money they are running on.
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.
The better analogy is the massive investment in fiber optic cable in the late 90s. All the companies in that line (Global Crossing, Worldcom etc.) went bust after investing 10s of billions but the capacity was useful (with a >90% drop in price) for future internet services. Around 2000 when the bubble was bursting only 5% of capacity was being used but proved to be useful to get all internet-first companies like the Googles, Amazon, NetFlix's going.
Good old JDS Uniphase was one of the first individual stocks I bought. I mean it had to go up right? Fiber and dark fiber and the continual threat of the internet collapsing due to load… better buy that stock!
I might be wrong, but my understanding is that we're on a decelerating slope of perf/transistor and have been for quite a while - I just looked up the OpenCL benchmark results of the 1080 Ti vs 4090, and the perf/W went up by 2.8x despite going from 16nm to 5nm, with perfect power scaling, we would've seen a more than 10x increase.
Probably not. There will be better GPUs. It's like we did use all those Kepler K10 and K80 fifteen or so years ago, they were Ok for models with few millions of parameters, then Pascal and Volta arrived ten years ago with massive speed up and larger memory, allowing to train same size models 2-4 times faster, so you simply had to replace all Keplers. Then Turing happened making all P100 and V100 obsolete. Then A100, and now H100. Next L100 or whatever with just more on-board memory will make H100 obsolete quickly.
One thing that is missing is that we have massively improved the performance of the algorithms lately to require less compute power, so a H100 will still be performant several years from now. The problem will be that it's going to be using up more power and physical space than an out-performing future version and so will need to be scrapped.
Think the FAANGs are doing part moat defending and part value add. Like AI powered spreadsheet might dethrone Excel so Excel has to be AI powered. MS will probably get some additional revenue from it but I don't think it will be a revolutionary amount.
> 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
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.
There is an old study that supports your point. The abstract reads:
"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."
Yes the "first mover advantage" is mostly just a common myth in business that refuses to die, but if we look at the original statement:
> 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.
Let's see:
Microsoft Windows: wasn't close to the first OS
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.
> Apple: First "smart phone" but not the first personal computer
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.
> I would call phones like the Palm Treo and later BlackBerries smartphones.
It's not just you; at the time these products were available, _everyone_ called them smartphones. Emphatically, Apple did not bring the first smartphone to market, not even close. They were, however, the first to popularize it beyond the field of nerds into the general public.
> There were even apps, but everything was lot more locked down and a lot more expensive.
And just plain... bad. The entire experience didn't have that "feel" that Apple turned into reality. It's comparable to today's AI landscape—the technology is pretty neat, but using it is a complete slog.
I actually have pretty fond memories of PalmOS PDAs. The hardware was very nice, but they were held back by the resistive touchscreen and dependence on a stylus for input. I never used a Treo but it felt like this was Palm trying to copy BlackBerry by adding a physical keyboard.
Edit: There were also the limitations of that era that held devices back in general. WAP internet[1] was awful, but most mobile services were too slow for much else.
In general, they were not. You're probably thinking of the very niche and unsuccessful Maemo/MeeGo project - eg Nokia N900 - that were indeed Linux-based. But everything else smartphone-ish from Nokia before Lumia (Windows Phone) were Symbian, which predates Linux and has nothing to do with it.
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.
I have a pet peeve with this common piece of wisdom. You can always find a "predecessor" for about anything. The corollary being that there is never a "first". And therefore, stating that "none of the big companies were the first in their categories" is just stating a tautology.
Maybe because the revenue isn't directly attributable to AI itself, but is realized in the cost savings and productivity improvements in already existing revenue streams? That's where AI has been useful to me. I can't put a number on how much AI has made me exactly, but it has certainly helped all aspects of my bootstrapped startup.
Any product should bring benefits to both the producer and the consumer.
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.
> but it has certainly helped all aspects of my bootstrapped startup.
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.
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.
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.
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.
> Okay I guess I've just had a different experience entirely.
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.
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.
>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.
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.
> Doctors can be associating what they’ve learned, often with heavy biases from hypochondriacs
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.
Will be cool once we have active agents tho. Surely the learning/research process isn't that difficult even for current LLMs/similar architectures. If it can teach itself, or it can collate new (never seen) data for other models then that's the cool part.
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.
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’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.
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.)
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.
>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.
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 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
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?
> While I only know R, which is obviously a simple language
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.
> 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).
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.
> If you can't have ChatGPT write testable code because of your architecture, you have other problems.
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.
>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.
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.
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.
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 :).
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).
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.)
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
> 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.
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.
> We aren't seeing higher overall levels of productivity.
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".
> We aren't seeing the developers who start using copilot/gpt rush ahead of their peers.
That's because individual productivity is usually constrained by team productivity. Devs rushing ahead of their teammates makes the team dysfunctional.
> We aren't seeing any ability to cut back on developer spend.
Devs aren't stupid. They're not going to give you an opportunity if they can avoid it.
> We aren't seeing anything positive yet and many developers have been using copilot/gpt for >1 year.
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.
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.
> We can even measure productivity improvements from Slacks/Zooms auto-summary features.
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.
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.
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.
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.
> How are you measuring developer productivity?
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.
> Were those that adopted copilot and chatgpt now enabled to finally keep up with their faster peers
No.
> Is developer satisfaction improved, and therefore retention?
> 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.
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
> 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)
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.
> 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.
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.
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.
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.
>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...
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".
> Can ChatGPT materially and positively impact the code written by big companies?
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.
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.
Leaving McKinsey's specific brand value aside, people always miss the value of "hiring (business) consultants". You basically get insider knowledge about how competitors businesses and systems work. So if you ask for advice about how to build a healthcare app for smartphones, you hire McKinsey (or whomever) to tell you "about the market". But really, they are just telling you about what they saw at other competitors. For some business decisions, it is very valuable.
> Being able to create 2048 in 6 hours has basically zero economic value.
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 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.
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.
I think you may be having a failure of imagination. That generation will be creating additional tooling to use this wherever possible, and taking advantage of any interoperability they or their peers can hack onto any interface. Our generation will be hesitant but they will not be, and many of the upcoming generation will have a deep understanding of where LLM’s have appropriate vs. awkward application.
> The fact that this power user class of young people hasn't found any other use cases for LLMs
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.
> 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.
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.
> We did not know mobile phones and e-commerce were going to be huge back in the 90s.
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!
> I'll admit it's totally possible LLMs and AI are going to be as earth shattering
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.
That's been true even for traditionally programmed replacements tho, there are plenty of office out there with a bunch of people banging on excel when everything they do could be automated.
> You can't know this for certain until you look back on it in retrospect.
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.
> Correct, but the thing is, AI blown up much faster than phones
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.
>> Correct, but the thing is, AI blown up much faster than phones
>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.
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.
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:
> Two years into the iPhone, Apple reported a $6.75b revenue on iPhone related sales.
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.
> 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.
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.
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.
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.
> I personally don't care what investors think and do.
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.
> It's not hype for regular Janes and Joes, who either already integrated ChatGPT into their daily lives, or see their friends doing so.
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.
> 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.
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.
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!
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.
And how much of an influence have you had on him to encourage or assist with this behaviour? What about the average person that doesn't know anybody (at least closely) working in tech.
> 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.
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."
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 :).
Or: work at an unnamed high frequency trading hedge fund, make $800K/yr. (The number of people on HN claiming to be this person surely exceeds the number in the Real World by ... multiples.)
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.
> Mobile phones weren't that useful early on, outside of niche cases.
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.
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.
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.
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 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.
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.
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.)
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".
The conversation then becomes more about the platform than about specific content.
Similar to a game; for many games players aren't intended to have exactly the same experience but there are still common things about the game platform to discuss.
I think it would be pretty cool to have a partially AI generated plot, it would be exciting to discuss what you got in the AI lottery with someone else who had also watched the show. Something like: [set plot point] [random catastrophic event] [set plot point] [etc]. "1 character must die in some meaningful way" turns to "omg, which one was killed when you watched? Adam got eaten by the giant spiders in mine" "oh man, Sarah tried to set a spider on fire in mine, but she ended up getting herself". A la https://en.wikipedia.org/wiki/Until_Dawn
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.
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.
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.
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 :-)
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'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.
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.
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?
> 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.
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.
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.
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.
> 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.
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).
> Even though I do think that almost any profession can potentially find use for LLMs in some shape or form.
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.
> 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.
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]. :-)"
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.
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.
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.
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.
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.
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.
> 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.
Companies will acrue value, but 'common folk' will lose it.
Self-serve checkout is an interesting example. Even though it can have its issues, I personally quite like being able to move through checkout more quickly, without having to interact with the cashier and the people in line in front of me (assuming enough self-serve stations).
I haven't analyzed its economics, but I would assume that by reducing the cashier jobs (e.g. allowing 1 operator to manage 8 self-serve checkout stations), supermarkets reduce their overall operating costs, and then use that to reduce their prices (and if they won't their competitors will), leading to a benefit to us 'common folk'.
As for cashier jobs, I don't think there's anything inherently good about them or inherently bad about them disappearing from the economy. As another example, I don't know many people who miss the Elevator Operators[0], and it makes perfect sense for us to press the buttons ourselves.
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.
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?
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?
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.
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.
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 :).
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
People have been comparing and contrasting successive technology innovation cycles for a long time. OP mentions railroads, but that doesn't mean there's an "effort to hitch" AI to railroads. It's just a comparison, which may or may not be useful in understanding how the latest innovation cycle may impact the world.
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.
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.
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.
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.
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.
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.
> They are more intelligent than the average person I deal with on a daily basis.
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.
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.
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 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.
>> 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.
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.
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.
> 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.
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?
> 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.
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.
> 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.
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.
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!
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.
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(?)
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.
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.
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.
Not just AI. AI is just the latest in hopeful technologies to get us out of this debt rut we are in. Every time something fails to get us out it drags us deeper in.
> Such strong speculative predictions about the future, with no evidence. How can anyone be so certain about this?
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.
> 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.
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?
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..
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.
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.
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.
> 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:
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.
Sure, if you are one of the rare engineers who wasn't using Google search, or any sort of discussions or collaborations with other engineers in their day to day engineering workflow, then I can fully understand why a super powered version of the same thing wouldn't be useful to you.
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
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.
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.
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.
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...
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.
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.
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.
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