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Do AI companies work?
- flappyeagle 2y agoThis is like when VCs were funding all kinds of ride share, bike share, food delivery, cannabis delivery, and burning money so everyone gets subsidized stuff while the market figures out wtf is going on. I love it. More goodies for us
- gimmefreestuff 2y agoAgree completely. Monetizing all of this is frankly...not my problem.
- leeter 2y agoI'm already keeping an eye on what NVidia gets into next... because that will inevitably be the "Next big thing". This is the third(ish) round of this pattern that I can recall, I'm probably wrong about the exact count, but NVidia is really good at figuring out how to be powering the "Next big thing". So alternatively... I should probably invest in the utilities powering whatever Datacenters are using the powerhungry monsters at the center of it all.
- malfist 2y agoThere's a saying in the stock market that probably applies here: past performance does not indicate future performance. Getting lucky twice is a row is really really lucky. Getting lucky three times in a row is not more likely because they were lucky two times in a row
- dartos 2y agoIt may be getting lucky, or it may be that they have really great leadership. Very few other large tech companies have deep technical competence at CEO level leadership
- furyofantares 2y agoIt's definitely both at the same time.
- Yizahi 2y agoHear me out, I know it is controversial idea, but anyway - gaming. :)
- mandevil 2y agoOne thing I'm not clear on is how much of this is cause and how much effect: that is, does NVidia cheerleading for something make it more popular with the tech press and then everyone else too? There are definitely large parts of the tech press that serve more as stenographers than as skeptical reporters, and so I'm not sure how much is NVidia picking the right next big thing and how much is NVidia announcing the next big thing to the rest of us?
- candiddevmike 2y agoNo, it means creating a bunch of unprofitable businesses that make it really hard for folks trying to build a sustainable business without VC money.
- JumpCrisscross 2y ago> that make it really hard for folks trying to build a sustainable business without VC money LLMs are capital intensive. They’re a natural fit for financing.
- jfengel 2y agoYep, you will probably lose. The VCs aren't out there to advance the technology. They are there to lay down bets on who's going to be the winner. "Winner" has little to do with quality, and rides much more on being the one that just happens to resonate with people. The ones without money will usually lose because they get less opportunity to get in front of eyeballs. Occasionally they manage it anyway, because despite the myth that the VCs love to tell, they aren't really great at finding and promulgating the best tech.
- denkmoon 2y agoWhat goodies can I get from AI companies though?
- candiddevmike 2y agoYour own GPL unencumbered regurgitations of popular GPL libraries and applications.
- lxgr 2y agoExcept that copyright law doesn’t work that way.
- mrbungie 2y agoAt least OAI and MSFT offer "copyright shields" for enterprise customers. Say what you want about the morals associated to said offerings, but it sounds very interesting for companies that may want to use a plagiarizing machine for copying GPL code while covering their asses in some way (at least in terms of cost).
- JumpCrisscross 2y ago> when VCs were funding all kinds of ride share, bike share, food delivery, cannabis delivery, and burning money so everyone gets subsidized stuff while the market figures out wtf is going on I’m reminded of slime molds solving mazes [1]. In essence, VC allows entrepreneurs to explore the solution space aggressively. Once solutions are found, resources are trimmed. [1] https://www.mbl.edu/news/how-can-slime-mold-solve-maze-physiology-course-finding-out https://www.mbl.edu/news/how-can-slime-mold-solve-maze-physi...
- gimmefreestuff 2y agoVC is the worst possible way to fund entrepeneurs. Except for all the others.
- JumpCrisscross 2y agoVC is good for high-risk, capital-intensive, scalable bets. The high risk and scalability cancel out, thereby leaving the core of finance: lending to enable economics of scale. Plenty of entrepreneurship is low to moderate risk, bootstrappable and/or unscalable. That describes where VC is a raw deal. It also does not describe AI.
- add-sub-mul-div 2y agoThat's exactly the short term thinking they're hoping they can use to distract. Tech companies purchased television away from legacy media companies and added (1) unskippable ads, (2) surveillance, (3) censorship and revocation of media you don't physically own, and now they're testing (4) ads while shows are paused. There's no excuse for getting fooled again.
- chillfox 2y agoWhere I live the ridesharing/delivering startups didn't bring goodies, they just made everything worse. They destroyed the Taxi industry, I used to be able to just walk out to the taxi rank and get in the first taxi, but not anymore. Now I have to organize it on an app or with a phone call to a robot, then wait for the car to arrive, and finally I have to find the car among all the others that other people called. Food delivery used to be done by the restaurants own delivery staff, it was fast, reliable and often free if ordering for 2+ people. Now it always costs extra, and there are even more fees if I want the food while it's still hot. Zero care is taken with the delivery, food/drinks are not kept upright and can be a total mess on arrival. Sometimes it's escaped the container and is just in the plastic bag. I have ended up preferring to go pickup food myself over getting it delivered, even when I have a migraine, it's just gone to shit.
- sqeaky 2y agoAdd the abuse of gig workers, expansion of the toxic tipping culture, increase in job count but reduction in pay, concentration of wealth in fewer hands. These rideshare and delivery companies are disgusting and terrible.
- CaptainFever 2y agoFor where I live (Asia), I disagree with both of these examples. Getting a taxi was awful before ride-sharing apps. You'd have to walk to a taxi stop, or wait on the side of the road and hope you could hail one. Once the ride-sharing apps came in, suddenly getting a ride became a lot simpler. Our taxi companies are still alive, though they have their own apps now -- something that wouldn't have happened without competition -- and they also work together with the ride-hailing companies as a provider. You could still hail taxis or get them from stops too, though that isn't recommended given that they might try to run the meter by taking a longer route. For food delivery, before the apps, most places didn't deliver food. Nowadays, more places deliver. Even if a place already had their own delivery drivers, they didn't get rid of them. We get a choice, to use the app or to use the restaurant's own delivery. Usually the app is better for smaller meals since it has a lower minimum order amount, but the restaurant provides faster delivery for bigger orders.
- 2y ago
- baq 2y agoFirst VC to exit a nuclear reactor profitably is going to be something.
- xenospn 2y agoUnless we’re trying to get an actual company funded without giving away our worthless product for free.
- Havoc 2y agoIt invariably end in enshitifcation of the startups product AND the destruction of the original option. Leaves you in a worse position than you started with.
- est 2y agoAI as a work force could be comparable to interns. They work in a 7x24 shift but fail the task from time to time.
- themanmaran 2y agoCompanies in the business of building models are forced to innovate on two things at once. 1. Training the next generation of models 2. Providing worldwide scalable infrastructure to serve those models (ideally at a profit) It's hard enough to accomplish #1, without worrying about competing against the hyperscalers on #2. I think we'll see large licensing deals (similar to Anthropic + AWS, OpenAI + Azure) as one of the primary income sources for the model providers. With the second (and higher margin) being user facing subscriptions. Right now 70% of OpenAI's revenue comes from chatgpt + enterprise gpt. I imagine Anthropic is similar, given the amount of investment in their generative UI. At the end of the day, model providers might just be consumer companies.
- AI_beffr 2y agothe hype will never die. all the smartest people in industry and government believe that there is a very high probability that this technology is near the edge of starting the AGI landslide. you dont need AGI to start the AGI landslide, you just need AI tools that are smart enough to automate the process of discovering and building the first AGI models. every conceivable heuristic indicates that we are near the edge. and because of this, AI has now become a matter of national security. the research and investment wont stop, because it cant, because it is now an arms race. this wont just fizzle out. it will be probed and investigated to absolute exhaustion before anyone feels safe enough to stop participating in the race. if you have been keeping up you will know that high level federal bureaucrats are now directly involved in openAI.
- umbra07 2y agocan you share some of these heuristics you referred to?
- AI_beffr 2y agothe turing test
- jfengel 2y agoI am not among those smartest, so take my opinion with a mountain of salt. But I'm just not convinced that this is going in the direction of AGI. The recent advances are truly jaw dropping. It absolutely merits being investigated to the hilt. There is a very good chance that it will end up being a net profit. But intuitively they don't feel to me like they're getting more human. If anything I feel like the recent round of "get it to reason aloud" is the opposite of what makes "general intelligence" a thing. The vast majority of human behavior isn't reasoned, aloud or otherwise. It'll be super cool if I'm wrong and we're just one algorithm or extra data set or scale factor of CPUs away from It, whatever It turns out to be. My intuition here isn't worth much. But I wouldn't be surprised if it was right despite that.
- snapcaster 2y ago
- bcherny 2y agoThis article, and all the articles like it, are missing most of the puzzle. Models don’t just compete on capability. Over the last year we’ve seen models and vendors differentiate along a number of lines in addition to capability: - Safety - UX - Multi-modality - Reliability - Embeddability And much more. Customers care about capability, but that’s like saying car owners care about horsepower — it’s a part of the choice but not the only piece.
- candiddevmike 2y agoTo me, the vast majority of "consumers" as in B2C only care about price, specifically free. Pro and enterprise customers may be more focused on the capabilities you listed, but the B2C crowd is vastly in the free tier only space when it comes to GenAI.
- bcherny 2y agoYou may be forgetting that ChatGPT has 10M paying customers. Not to mention everyone that pays for Claude Pro, Perplexity Pro, and so on.
- cratermoon 2y ago> OpenAI has 10M paying customers According to who?
- bcherny 2y ago> OpenAI COO Says ChatGPT Passed 11 Million Paying Subscribers https://www.theinformation.com/articles/openai-coo-says-chatgpt-passed-11-million-paying-subscribers https://www.theinformation.com/articles/openai-coo-says-chat...
- wslh 2y agoNot OP but what is your guess? Bloomberg says 1M customers in the business plan [1]. [1] https://www.bloomberg.com/news/articles/2024-09-05/openai-hits-1-million-paid-users-for-business-version-of-chatgpt https://www.bloomberg.com/news/articles/2024-09-05/openai-hi...
- streetcat1 2y agoThe competition for big LLM AI companies is not other big LLM AI companies, but rather small LLM AI companies with good enough models. This is a classic innovator dilemma. For example, I can imagine a team of cardiologists creating a fine tune LLM model.
- Terr_ 2y agoWhat on earth would cardiologists use a Large Language Model for, except drafting fluff for journals? Safely and effectively, that is. Dangerous and inappropriate is obviously a much wider set of possibilities.
- Leherenn 2y agoLike some kind of linter? The cardiologist checks the ECG, compare with the LLM results and checks the difference. If it can reduce error rate by like 10%, that's already really good. My current stance on LLM is that it's good for stuff which is painful to generate, but easy to check (for you). It's easier/faster to read an email than to write it. If you're a domain expert, you can check the output, and so on. The danger is in using it for stuff you cannot easily check, or trusting it implicitly because it is usually working.
- SoftTalker 2y ago> trusting it implicitly because it is usually working I think this danger is understated. Humans are really prone to developing expectations based on past observations and then not thinking very critically about or paying attention to those things once those expectations are established. This is why "self driving" cars that work most of the time but demand that the driver remain attentive and prepared to take over are such a bad idea.
- Terr_ 2y ago> The cardiologist checks the ECG, compare with the LLM results and checks the difference. Perhaps you're confusing the acronym LLM (Large Language Model) with ML (Machine Learning)? Analyzing electrocardiogram waveform data using a text-predictor LLM doesn't make sense: No matter how much someone invests in tweaking it to give semi-plausible results part of the time, it's fundamentally the wrong tool/algorithm for the job.
- cratermoon 2y agoI would say, without qualification, "no". https://www.techpolicy.press/challenging-the-myths-of-generative-ai/ https://www.techpolicy.press/challenging-the-myths-of-genera...
- Der_Einzige 2y ago#2 is dead wrong, and shows that the author is not aware of the current exciting research happening in parameter efficient fine-tuning or representation/activation engineering space. The idea that you need huge amounts of compute to innovate in a world of model merging and activation engineering shows a failure of imagination, not a failure to have the necessary resources. PyReft, Golden Gate Claude (Steering/Control Vectors), Orthogonalization/Abliteration, and the hundreds of thousands of Lora and other adapters available on websites like civit.ai is proof that the author doesn't know what they're talking about re: point #2. And I'm not even talking about the massive software/hardware improvements we are seeing for training/inference performance. I don't even need that, I just need evidence that we can massively improve off the shelf models with almost no compute resources, which I have.
- llm_trw 2y agoI've found that everything that works stops being called AI. Logic programming? AI until SQL came out. Now it's not AI. OCR, computer algebra systems, voice recognition, checkers, machine translation, go, natural language search. All solved, all not AI any more yet all were AI before they got solved by AI researchers. There's even a name for it: https://en.m.wikipedia.org/wiki/AI_effect?utm_source=perplexity https://en.m.wikipedia.org/wiki/AI_effect?utm_source=perplex...
- AlienRobot 2y agoThat's a very interesting phenomenon!
- riku_iki 2y ago> Logic programming? AI until SQL came out. Now it's not AI. logic programming is not directly linked to SQL, and has its own AI term now: https://en.wikipedia.org/wiki/GOFAI https://en.wikipedia.org/wiki/GOFAI.
- llm_trw 2y agoThe original SQL was essentially Prolog restricted to relational algebra and tuple relational calculus. SQL as is happened when a lot of cruft was added to the mathematical core.
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- analog31 2y agoOn an amusing note, I've read something similar: Everything that works stops being called philosophy. Science and math being the two familiar examples.
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- plaidfuji 2y agoI get the sense that the value prop of LLMs should first be cut into two categories: coding assistant, and everything else. LLMs as coding assistants seem to be great. Let’s say that every working programmer will need an account and will pay $10/month (or their employer will).. what’s a fair comp for valuation? GitHub? That’s about $10Bn. Atlassian? $50Bn The “everything else” bin is hard to pin down. There are some clear automation opportunities in legal, HR/hiring, customer service, and a few other fields - things that feel like $1-$10Bn opportunities. Sure, the costs are atrocious, but what’s the revenue story?
- dartos 2y ago> I get the sense that the value prop of LLMs should first be cut into two categories: coding assistant, and everything else. Replace coding assistant with artists and you have the vibe of AI 2 years ago. The issue is that these models are easy to make (if expensive) so the open source community (of which many, maybe most, are programmers themselves) will likely eat up any performance moat given enough time. This story already played out with AI art. Nothing beats SD and comfyUI if you really need high quality and control.
- fnordpiglet 2y agoHaving been there for the dotcom boom and bust from pre-IPO Netscape to the brutal collapse of the market, it’s hard to say dotcoms don’t work. There was clearly something there of immense value, but it took a lot experimentation with business models and maturation of technology as well as the fundamental communications infrastructure of the planet. All told it feels like we’ve really only gained a smooth groove in the last 10 years. I see no reason why AI will be particularly different. It seems difficult to make the case AI is useless, but it’s also not particularly mature with respect to fundamental models, tool chains, business models, even infrastructure. In both cases speculative capital flowed into the entire industry, which brought us losers like pets.com but winners like Amazon.com, Netflix.com, Google.com, etc. Which of the AI companies today are the next generation of winners and losers? Who knows. And when the music stops will there be a massive reckoning? I hope not, but it’s always possible. It probably depends on how fast we converge to “what works,” how many grifters there are, how sophisticated equity investors are (and they are much more sophisticated now than they were in 1997), etc.
- cageface 2y agoIt seems very difficult to build a moat around a product when the product is supposed to be a generally capable tool and the input is English text. The more truly generally intelligent these models get the more interchangeable they become. It's too easy to swap one out for another.
- nine_k 2y agoHumans are the ultimate generally intelligent agents available on this planet. Even though most of them (us) are replaceable for mundane tasks, quite some are unique enough so that people seek their particular services and no one else's. And this is among the pool of about eight billion such agents.
- cageface 2y agoHow is this relevant to AI? An AI can be trained on the total knowledge of every field.
- nine_k 2y agoIt's even more relevant to AI, because the differences between the models (not just training data) may make them pretty uniquely suited to some areas, with any competition being markedly worse (in that area), or at least markedly different. And you only have maybe dozens, not billions, to choose from.
- lmm 2y ago> Even though most of them (us) are replaceable for mundane tasks, quite some are unique enough so that people seek their particular services and no one else's. Very few people manage that - indeed I can't think of anyone. Even movie stars get replaced with other movie stars if they try to charge too much. Certainly everyone in the tech industry (including the CEOs, the VCs, the investors etc.) has a viable substitute.
- fsndz 2y agoLLMs are basically becoming commodities: https://www.lycee.ai/blog/why-large-language-models-are-a-commodity-now https://www.lycee.ai/blog/why-large-language-models-are-a-co...
- patrickhogan1 2y agoChatGPT benefits from network effects, where user feedback on the quality of its answers helps improve the model over time. This reduces its reliance on external services like ScaleAI, lowering development costs. Larger user base = increased feedback = improved quality of answers = moat
- 23B1 2y agoIt is ironic that this article seems to focus on the business logic, considering that is the same myopia at these AI companies. Not that physical/financial constraints are unimportant, but they often can be mitigated in other ways. Some background: I was previously at one of these companies that got hoovered up in the past couple years by the bigs. My job was sort of squishy, but it could be summarized as 'brand manager' insofar as it was my job to aide in shaping the actual tone, behaviors, and personality of our particular product. I tell you this because in full disclosure, I see the world through the product/marketing lens as opposed to the engineer lens. They did not get it. And by they I mean founds whose names you've heard of, people with absolute LOADS of experience in building a shipping technology products. There were no technical or budgetary constraints at this early stage, we were moving fast and trying shit. But they simply could not understand why we needed to differentiate and how that'd make us more competitive. I imagine many technology companies go through this, and I don't blame technical founders who are paranoid about this stuff; it sounds like 'management bullshit' and a lot of it is, but at some point all organizations who break even or take on investors are going to be answerable to the market, and that means leaving no stone unturned in acquiring users and new revenue streams. All of that to say, I do think a lot of these AI companies have yet to realize that there's a lot to be done user experience-wise. The interface alone - a text prompt(!?) is crazy out-of-touch to me. The fact that average users have no idea how to set up a good prompt and how hard everyone is making it for them to learn about that. All of these decisions are pretty clearly made by someone who is technology-oriented, not user-oriented. There's no work I'm aware of being done on tone, or personality frameworks, or linguistics, or characterization. Is the LLM high on numeracy? Is it doing code switching/matching, and should it? How is it qualifying its answers by way of accuracy in a way that aids the user learning how to prompt for improved accuracy? What about humor or style? It just completely flew over everyone's heads. This may have been my fault. But I do think that the constraints you see to growth and durability of these companies will come down to how they're able to build a moat using strategies that don't require $$$$ and that cannot be easily replicated by competition. Nobody is sitting in the seats at Macworld stomping their feet for Sam Altman. A big part of that is giving customers more than specs or fiddly features. These companies need to start building a brand fast.
- SamBam 2y ago> If the proprietary models stop moving forward, the open source ones will quickly close the gap. This is the Red Queen hypothesis in evolution. You have to keep running faster just to stay in place. On it's face, this does seem like a sound argument that all the $$ following LLMs is irrational: 1. No matter how many billions you pour into your model, you're only ever, say, six months away from a competitor building a model that's just about as good. And so you already know you're going to need to spend an increased number of billions next year. 2. Like the gambler who tries to beat the house by doubling his bet each time, at some point there must be a number where that many billions is considered irrational by everybody. 3. Therefore it seems irrational to start putting in even the fewer billions of dollars now, knowing the above two points.
- JumpCrisscross 2y ago> it seems irrational to start putting in even the fewer billions of dollars now, knowing the above two points This doesn’t follow. One, there are cash flows you can extract in the interim—standing in place is potentially lucrative. And two, we don’t know if the curve continues until infinity or asymptotes. If it asymptotes, being the first at the asymptote means owning a profitable market. If it doesn’t, you’re going to get AGI. Side note: bought but haven’t yet read The Red Queen. Believe it was a comment of yours that lead me to it.
- jsnell 2y agoWhy would being the first at the (horizontal) asymptote mean owning a profitable market? In six months, somebody else is at the asymptote as well. If different models are mostly interchangable quality-wise, then the decision will mostly be made on price. For a market to be both winner-take-all as well as lucrative, you need some kind of a feedback cycle from network effets, economies of scale, or maybe even really brutal lock-in. For example, for operating systems applications provide a network effect -- more apps means an OS will do better, a higher install base for an OS means it'll attract more apps. For social networks it's users. One could imagine this being the case for LLMs; e.g. that LLMs with a lot of users can improve faster. But if the asymptote has been reached, then by definition there's no more improvement to be had, and none of this matters.
- gdiamos 2y agoThe top of the nasdaq is full of companies that build computers (from phones to data centers), not companies that only do algorithms. A clever AI algorithm run on rented compute is not a moat.
- MangoCoffee 2y ago>Therefore, if you are OpenAI, Anthropic, or another AI vendor, you have two choices. Your first is to spend enormous amounts of money to stay ahead of the market. This seems very risky though Regarding point 6, Amazon invested heavily in building data centers across the US to enhance customer service and maintain a competitive edge. it was risky. This strategic move resulted in a significant surplus of computing power, which Amazon successfully monetized. In fact, it became the company's largest profit generator. After all, startups and businesses is all about taking risk, ain't it?
- donavanm 2y agoNo, this an incorrect analogy on most points. Amazon retail marketplaces are absolutely not correlated with distributed data centers. Most markets are served out of a single cluster, which is likely to be in a different country; eg historically all of Europe from dublin, the americas from virginia, and asia pacific from seattle/portland. The move of the retail marketplace hosting from seattle to virginia happened alongside, and continued long after, the start of AWS. It is an utter myth, not promoted by Amazon, that AWS was some scheme to use “surplus computing power” from retail hosting/operations. It was an intentional business case to get in to a different B2B market as a service provider.
- lukev 2y agoYes, the "surplus compute power" story doesn't hold water. The magic of AWS is in its software and control plane.
- agentultra 2y agoThey eventually have to turn a profit or pass the hot potato. Maybe they’ll be the next generation of oligarchs supported by the state when they all go bankrupt but are too essential to fail. My guess is that profitability is becoming increasingly difficult and nobody knows how yet… or whether it will be possible. Seems like the concentration of capital is forcing the tech industry to take wilder and more ludicrous bets each year.
- rurban 2y agoDon't mixup LLM with AI. Not every AI company works on top of LLM's, many are doing vision or robotics or even old-school AI. Our system works, is AI, is profitable, doing vision. Vision scales. There's a little bit of LLM classification. And robotics also, but this part is not really AI, just a generic industry robot.
- highfrequency 2y agoEnjoyed the article and thought many of the points were good. Here's a counterargument. > In other words, the billions that AWS spent on building data centers is a lasting defense. The billions that OpenAI spent on building prior versions of GPT is not, because better versions of it are already available for free on Github. The money that OpenAI spends on renting GPUs to build the next model is not what builds the moat. The moat comes from the money/energy/expertise that OpenAI spends on the research and software development. Their main asset is not the current best model GPT-4; it is the evolving codebase that will be able to churn out GPT-5 and GPT-6. This is easy to miss because the platform can only churn out each model when combined with billions of dollars of GPU spend, but focusing on the GPU spend misses the point. We're no longer talking about a thousand line PyTorch file with a global variable NUM_GPUs that makes everything better. OpenAI and competitors are constantly discovering and integrating improvements across the stack. The right comparison is not OpenAI vs. AWS, it's OpenAI vs. Google. Google's search moat is not its compute cluster where it stores its index of the web. Its moat is the software system that incorporates tens of thousands of small improvements over the last 20 years. And similar to search, if an LLM is 15% better than the competitors, it has a good shot at capturing 80%+ of the market. (I don't have any interest in messing around with a less capable model if a clearly better one exists.) Google was in some sense "lucky" that when they were beginning to pioneer search algorithms, the hardware (compute cluster) itself was not a solved problem the way it is today with AWS. So they had a multidimensional moat from the get-go, which probably slowed early competition until they had built up years' worth of process complexity to deter new entrants. Whereas LLM competition is currently extremely fierce for a few reasons: NLP was a ripe academic field with a history of publishing and open source, VC funding environment is very favorable, and cloud compute is a mature product offering. Which explains why there is currently a proliferation of relatively similar LLM systems: > Every LLM vendor is eighteen months from dead. But the ramp-up time for competitors is only short right now because the whole business model (pretrain massive transformers -> RLHF -> chatbot interface) was only discovered 18 months ago (ChatGPT launched at the end of 2022) - and at that point all of the research ideas were published. By definition, the length of a process complexity moat can't exceed how long the incumbent has been in business! In five years, it won't be possible to raise a billion dollars and create a state of the art LLM system, because OpenAI and Anthropic will have been iterating on their systems continuously. Defections of senior researchers will hurt, and can speed up competitor ramp-time slightly, but over time a higher proportion of accumulated insights is stored in the software system rather than the minds of individual researchers. Let me emphasize: the billions of dollars of GPU spend is a distraction; we focus on it because it is tangible and quantifiable, and it can feel good to be dismissive and say "they're only winning because they have tons of money to simply scale up models." That is a very partial view. There is a tremendous amount of incremental research going on - no longer published in academic journals - that has the potential to form a process complexity moat in a large and relatively winner-take-all market.
- bhouston 2y agoI think we are in the middle of a steep S-curve of technology innovation. It is far from plateauing and there are still a bunch of major innovations that are likely to shift things even further. Interesting time and these companies are riding a wild wave. It is likely some will actually win big, but most will die - similar to previous technology revolutions. The ones that win will win not just on technology, but on talent retention, business relationships/partnerships, deep funding, marketing, etc. The whole package really. Losing is easy, miss out on one of these for a short period of time and you've easily lost. There is no major moat, except great execution across all dimensions.
- ninetyninenine 2y agoLikely we are because the actualized end result already exists: the human brain. The fact that human intelligence exists means that the idea of human level intelligence is not a pipe dream. The question is whether or not the basic underlying technology of the LLM can achieve that level of intelligence.
- thfuran 2y agoAnd brains do it on about 20 watts.
- asterix_pano 2y agoTraining takes many years though.
- martin_drapeau 2y agoThe fundamental question is how to monetize AI? I see 2 paths: - Consumers - the Google way: search and advertise to consumers - Businesses - the AWS way: attrack businesses to use your API and lock them in The first is fickle. Will OpenAI become the door to the Internet? You'll need people to stop using Google Search and rely on ChatGPT for that to happen. Will become a commodity. Short term you can charge a subscription but long term will most likely become a commondity with advertising. The second is tangible. My company is plugged directly to the OpenAI API. We build on it. Still very early and not so robust. But getting better and cheaper and faster over time. Active development. No reason to switch to something else as long as OpenAI leads the pack.
- MOARDONGZPLZ 2y agoBut OpenAI doesn’t lead the pack. How do you determine when to switch or when to just keep going with (potentially marginally) inferior product?
- atomsatomsatoms 2y agoThere would need to be significant capabilities that openai doesn't have or wouldn't be built on a short-ish timeline to have the enterprise switch. There's tons of bureaucratic work going on behind the scenes to approve a new vendor.
- martin_drapeau 2y agoSure it does. Ask any common mortal about AI and they'll mention ChatGPT - not Claude, Gemini or whatever else. They might not even know OpenAI. But they do know ChatGPT. Has it become a verb yet? Waiting to peole to replace "I googled how to..." with "I chatgpted how to...".
- vrosas 2y agoI see that a lot already. “I asked ChatGPT for a list of places to visit in Vermont and we planned our road trip around that!”
- janoc 2y agoThe mistake in that article is the assumption that these companies collecting those gigantic VC funding rounds are looking to stay ahead of the pack and be there even 10 years down the road. That's a fundamental misunderstanding of the (especially) US startup culture in the last maybe 10-20 years. Only very rarely is the goal of the founders and angel investors to build an actual sustainable business. In most cases the goal is to build enough perceived value by wild growth financed by VC money & by fueling hype that an subsequent IPO will let the founders and initial investors recoup their investment + get some profit on top. Or, find someone to acquire the company before it reaches the end of its financial runway. And then let the poor schmucks who bought the business hold the bag (and foot the bill). Nobody cares if the company becomes irrelevant or even goes under at that point anymore - everyone who did has has recouped their expense already. If the company stays afloat - great, that's a bonus but not required.
- goldfeld 2y agoNo, clearly, AIs do all the work for them.
- dfilppi 2y ago[dead]
- bradhilton 2y agoKind of feels like the ride-sharing early days. Lots of capital being plowed into a handful of companies to grab market share. Economics don't really make sense in the short term because the vast majority of cash flows are still far in the future (Zero to One). In the end the best funded company, Uber, is now the most valuable (~$150B). Lyft, the second best funded, is 30x smaller. Are there any other serious ride sharing companies left? None I know of, at least in the US (international scene could be different). I don't know how the AI rush will work out, but I'd bet there will be some winners and that the best capitalized will have a strong advantage. Big difference this time is that established tech giants are in the race, so I don't know if there will be a startup or Google at the top of the heap. I also think that there could be more opportunities for differentiation in this market. Internet models will only get you so far and proprietary data will become more important potentially leading to knowledge/capability specialization by provider. We already see some differentiation based on coding, math, creativity, context length, tool use, etc.
- SoftTalker 2y agoIt took 15 years for Uber to turn a profit. Will AI investors have that much patience?
- Loughla 2y agoWhy wouldn't they? Uber is just cabs but without the overhead. AI offers WAAAAYYYYY more money in the future.
- dj_axl 2y agoYou're perhaps forgetting Waymo at $30B
- marcosdumay 2y agoIs Uber profitable already or are they waiting for another order of magnitude increase in scale before they bother with that? Amazon is the poster-child of that mentality. It spent more than it earned into growth for more than 20 years, got a monopoly on retail, and still isn't the most profitable retail company around.
- matchagaucho 2y ago>2) pushing the frontier further out will likely get more difficult. The upside risk is premised on this point. It'll get so cost prohibitive to build frontier models that only 2-3 players will be left standing (to monetize).
- LASR 2y agoI lead an applied AI research team where I work - which is a mid-sized public enterprise products company. I've been saying this in my professional circles quite often. We talk about scaling laws, superintelligence, AGI etc. But there is another threshold - the ability for humans to leverage super-intelligence. It's just incredibly hard to innovate on products that fully leverage superintelligence. At some point, AI needs to connect with the real world to deliver economically valuable output. The ratelimiting step is there. Not smarter models. In my mind, already with GPT-4, we're not generating ideas fast enough on how best to leverage it. Getting AI to do work involves getting AI to understand what needs to be done from highly bandwidth constrained humans using mouse / keyboard / voice to communicate. Anyone using a chatbot already has felt the frustration of "it doesn't get what I want". And also "I have to explain so much that I might as well just do it myself" We're seeing much less of "it's making mistakes" these days. If we have open-source models that match up to GPT-4 on AWS / Azure etc, not much point to go with players like OpenAI / Anthropic who may have even smarter models. We can't even use the dumber models fully.
- datavirtue 2y ago"In my mind, already with GPT-4, we're not generating ideas fast enough on how best to leverage it." This is the main bottle neck, in my kind. A lot of people are missing from the conversation because they don't understand AI fully. I keep getting glimpses of ideas and possibilities and chatting through a browser ain't one of them. On e we have more young people trained on this and comfortable with the tech and understanding it, and existing professionals have light bulbs go off in their heads as they try to integrate local LLMs, then real changes are going to hit hard and fast. This is just a lot to digest right now and the tech is truly exponential which makes it difficult to ideate right now. We are still enveloping the productivity boost from chatting. I tried explaining how this stuff works to product owners and architects and that we can integrate local LLMs into existing products. Everyone shook their head and agreed. When I posted a demo in chat a few weeks later you would have thought the CEO called them on their personal phone and told them to get on this shit. My boss spent the next two weeks day and night working up a demo and presentation for his bosses. It went from zero to 100kph instantly.
- shahzaibmushtaq 2y agoYes, AI companies can only work if they all somehow agree to slow things down a little bit instead of competing to release a better model like every month.
- darajava 2y agoWhy would this make them work?
- shahzaibmushtaq 2y agoThe answer lies in why NFT companies never worked.
- culebron21 2y agoI think the article contradicts itself, missing some simple math. Contradicting points: 1. it takes huge and increasing costs to build newer models, models approached asymptote 2. a startup can take an open-source model and get you out of business in 18 months (with CocaCola example) The size of LLM is what protects them from being attacked by startups. Microsoft's operating profit in 2022 was $72B, which is 10x bigger than the running cost of OpenAI. And if 2022 was too successful, profits of $44B still dwarf OpenAI. If OpenAI manages to ramp up investment like Uber, it may stay alive, otherwise it's tech giants that can afford running some LLM. ...if people will be willing to pay for this level of quality (well, if you integrate it into MS Word, they actually may want it).
- Nemi 2y agoSo riddle me this - Netflix’s revenue is around $9.5B. So a little more than the running costs of OpenAI. Do you think it is plausible that they will be able to get enough people to pay for OpenAI that Netflix? They would need much more revenue than Netflix to make it viable. Considering that there are other options available, like Google’s, MSFT’s, Anthropic’s, etc. As a business model, this is all very suspect. GPT5 has had multiple delays and challenges. What if “marginally better” is all we are going to get? Just food for thought.
- Tarsul 2y agoThat's Netflix's revenue for a single quarter, not year. (nonetheless, your point is relevant)
- arbuge 2y ago> What, then, is an LLM vendor’s moat? Brand? Inertia? A better set of applications built on top of their core models? An ever-growing bonfire of cash that keeps its models a nose ahead of a hundred competitors? Missed one I think... the expertise accumulated in building the prior generation models, that are not themselves that useful anymore. Yes, it's true that will be lost if everybody leaves, a point he briefly mentions in the article. But presumably AWS would also be in trouble, sooner or later, if everybody who knows how things work left. Retaining at least some good employees is tablestakes for any successful company long-term. Brand and inertia also don't quite capture the customer lock-in that happens with these models. It's not just that you have to rewrite the code to interface with a competitor's LLM; it's that that LLM might now behave very differently than the one you were using earlier, and give you unexpected (and undesirable) results.
- sillyLLM 2y agoI think a selling point for LLMs would be to match you with people that you find perfect for that use case. For example a team for a job, a wife, real friends, clubs for sharing hobbies, for finding the best people mastering something you want to accomplish. Unfortunately we and LLMs don't know how to match people in that way.
- w10-1 2y agoTwo models to address how/why/when AI companies make sense: (1) High integration (read: switching) costs: any deployment of real value is carefully tested and tuned for the use-case (support for product x, etc.). The use cases typically don't evolve that much, so there's little benefit to re-incurring the cost for new models. Hence, customers stay on old technology. This is the rule rather than the exception e.g., in medical software. (2) The Instagram model: it was valuable with a tiny number of people because they built technology to do one thing wanted by a slice of the market that was very interesting to the big players. The potential of the market set the time value of the delay in trying to replicate their technology, at some risk of being a laggard to a new/expanding segment. The technology gave them a momentary head start when it mattered most. Both cases point to good product-market fit based on transaction cost economics, which leads me to the "YC hypothesis": The AI infrastructure company that best identifies and helps the AI integration companies with good product-market fit will be the enduring leader. If an AI company's developer support consist of API credits and online tutorials about REST API's, it's a no-go. Instead, like YC and VC's, it should have a partner model: partners use considerable domain skills to build relationships with companies to help them succeed, and partners are selected and supported in accordance with the results of their portfolio. The partner model is also great for attracting and keeping the best emerging talent. Instead of years of labor per startup or elbowing your way through bureaucracies, who wouldn't prefer to advise a cohort of the best prospects and share their successes? Unlike startup's or FAANG, you're rewarded not for execution or loyalty, but for intelligence in matching market needs. So the question is not whether the economics of broadcast large models work, but who will gain the enduring advantage in supporting AI eating the software that eats the world?
- charlieyu1 2y agoTSMC is by large the biggest chip company of the world and they are still investing lots of money in research.
- cutemonster 2y ago> Your second choice is… I don’t know? What about: Lobby for AI regulations that prevent new competitors from arising, and hopefully kills of a few?
- valine 2y agoThis period of model scaling at all cost is going to be a major black eye on the industry in a couple years. We already know that language models are few shot learners at inference time, and yet OpenAI seems to be happy throwing petaflops of compute training models the slow way. The question is how can you use in-context learning to optimize the model weights. It’s a fun math problem and it certainly won’t take a billion dollar super computer to solve it.
- johnsutor 2y agoSeems like this is already being answered: https://arxiv.org/abs/2407.10930 https://arxiv.org/abs/2407.10930 https://arxiv.org/abs/2006.04439 https://arxiv.org/abs/2006.04439
- valine 2y agoNot really the first paper is just fine-tuning on synthetic data. The second paper doesn’t optimize the model weights.
- tinyhouse 2y agoI like the article and agree with many of the arguments. A few comments though. 1. It's not that easy to switch between providers. There's no lock-in of course, but once you build a bunch of code that is provider specific (structured outputs, prompt caching, json mode, function calls, prompts designed for a specific provider, specific tools used by the openai Assistant, etc) then you need a good reason to switch (like a much better or cheaper model) 2. All of these companies do try to build some echo system around them, esp in the enterprise. The problem is that Google and Microsoft have a huge advantage here cause they have all the integrations 3. The consumer side. It's not just LLM. It's image, video, voice, and many more. You cannot ignore that ChatGPT can rival Google in a few years in terms of usage. As long as they can deliver good models, users are not going to switch so quickly. It's a huge market, just like Google. Pretty much, everyone in the world is going to use ChatGPT or some alternative in the next few years. My 9 year old and her friends already use it. No reason why they cannot monetize their huge user base like Google did.
- winddude 2y agoprobably why OpenAI wants to build there own 5gw nuclear plant.
- arnaudsm 2y agoLLM intelligence has plateaued for the past 12 months. Open source models are catching up while being 20x smaller than the original gpt4 (gemma2/llama3.2/qwen2.5). AI companies are promising AGI to investors to survive a few more years before they probably collapse and don't deliver on that promise. LLMs are now a commodity. It's time for startups to build meaningful products with it!
- mepian 2y agoThe title reminds me of this classic paper, "If It Works, It's Not AI: A Commercial Look at Artificial Intelligence Startups": https://dspace.mit.edu/handle/1721.1/80558 https://dspace.mit.edu/handle/1721.1/80558
- tqi 2y agoPersonally, I think the more interesting question is: if not AI then what? I doubt we will go back to Metaverse (lol) or Crypto, B2B SaaS seems safe but stagnant (and competing over a largely fixed pie of IT spend). It feels like it doesn't matter if the companies work-- until there is something shinier to invest in it will be the defacto choice for LPs chasing returns (and VCs chasing carry).
- britzsquad 2y agoHow about a good old financial crisis?
- tqi 2y agoThe people will demand a new bubble to invest in: https://theonion.com/recession-plagued-nation-demands-new-bubble-to-invest-i-1819569940/ https://theonion.com/recession-plagued-nation-demands-new-bu...
- pier25 2y agoWhat about nothing? Only vcs need something new to hype.
- freetonik 2y agoHow about improving the software, both the development process and the final product? We hear claims of upcoming AGI, but most everyday software still feels embarrassingly broken.
- PodgieTar 2y agoHow about moving away from the unicorn model into creating small, sustainable and focused companies that don't explode, but provide sustainable income.
- tqi 2y agoLol that sounds good to me, but I'm not the one making those decisions. To be clear, my comment was a prediction based on what I see as the primary motivations of the people with the money. I'm definitely not endorsing this model.
- paulvnickerson 2y agoAnd here I am spending all my time with boring xgboost classifiers...
- willmadden 2y agoLet's hope the moat isn't regulatory...
- ryukoposting 2y agoI don't really buy the "cost of hardware" portion of the argument, even if everything else seems sound. In 1992 if you wanted 3D graphics, you'd call up SGI and drop $25,000 on an Indigo 2. 6 years later, you could walk into Circuit City and buy a Voodoo2 for 300 bucks, slap it in the PC you already owned, and call it a day. I know we aren't in the 90s. I know that the cost of successive process nodes has grown exponentially, even when normalizing for inflation. But, still. I'd be wary of betting the farm on AI being eternally confined to giant, expensive special-purpose hardware. This stuff is going to get crammed into a little special purpose chip dangling off your phone's CPU. Either that, or GPU compute will become so commodified that it'll be a cheap throw-in for any given VPS.
- sobellian 2y agoThis stuff is going to get crammed through an algorithm orders of magnitude more efficient. We have living proof that it does not take 5GW to make a brain.
- 29athrowaway 2y agoJust like supermarkets know what products you are buying, LLM inference providers what requests you are making. And just like supermarkets list the most profitable products and then clone them, LLM inference providers could come up with their own versions of the most profitable products based on LLMs. My prediction is that the thin layer built on top of LLMs will be eaten up starting from the most profitable products. By using inference APIs you are doing their market research for free.
- ocean_moist 2y agoThe title should be “Do LLM building companies work?”. The article fails to address companies that will be using LLMs or companies innovating on other models/architectures. I don’t think most people looking to build an AI company want to build an LLM and call it a company.
- jillesvangurp 2y agoThe question is too broad. What's an AI company? It could be anything. The particular sub class here that is implied is companies that are spending many billions to develop LLMs. The business model for those is to produce amazing LLM models that are hard to re-create unless you have similar resources and then make money providing access, licensing, etc. What are those resources? Compute, data, and time. And money. You can compensate for lack of time by throwing compute at the problem or less/more data. Which is a different way of saying: spend more money. So, it's no surprise that this space is dominated by trillion dollar companies with near infinite budgets and a small set of silicon valley VC backed companies that are getting multi billion dollar investments. So the real question is whether these companies have enough of a moat to defend their multi billion dollar investments. The answer seems to be no. For three reasons: hardware keeps getting cheaper, software keeps getting better, and using the models is a lot cheaper than creating them. Creating GPT-3 was astronomically expensive a few years ago and now it is a lot cheaper by a few orders of magnitude. GPT-3 is of course obsolete now. But I'm running Llama 3.2 on my laptop and it's not that bad in comparison. That only took 2 years. Large scale language model creation is becoming a race to the bottom. The software is mostly open source and shared by the community. There is a lot of experimentation happening but mostly the successful algorithms, strategies, and designs are quickly copied by others. To the point where most of these companies don't even try to keep this a secret anymore. So that means new, really expensive LLMs have a short shelf life where competitors struggle to replicate the success and then the hardware gets cheaper and others run better algorithms against whatever data they have. Combine that with freely distributed models and the ability to run them on cheap infrastructure and you end up with a moat that isn't that hard to cross. IMHO all of the value is in what people do with these models. Not necessarily in the models. They are enablers. Very expensive ones. Perhaps a good analogy is the value of Intel vs. that of Microsoft. Microsoft made software that ran on Intel chips. Intel just made the chips. And then other chip manufacturers came along. Chips are a commodity now. Intel is worth a lot less than MS. And MS is but a tiny portion of the software economy. All the value is in software. And a lot of that software is OSS. Even MS uses Linux now.
- coding123 2y agoI don't get the point of the author. At one point he's saying because the race to get bigger and better will never end, we'll need ever larger compute and more and more and more, so in the end, the companies will fail. I don't see it this way. In plumbing I could have chosen to use 4" pipe throughout my house. I chose 3". Heck, I could have purchased commercial pipe that's 12", or even 36". It would have changed a lot of the design of my foundation. Just because there is something much bigger and can handle a lot more poop, doesn't mean it's going to be useful for everyone.
- Nevermark 2y ago> it’s burning through $7 billion a year to fund research and new A.I. services and hire more employees And at some point one of these companies will reach the point it does not need as many employees. And has a model capable of efficiently incorporating new learning without having to reset and relearn from scratch. That is what AGI is. Computing resources for inference and incremental learning will still be needed, but when the AGI itself is managing all/much of that, including continuing to find efficiencies, ... profitably might be unprecedented. The speed of advance over the last two decades has been steady and exponential. There are not many (or any) credible signals that a technical wall is about to be encountered. Which is why I believe that I, I by myself, might get there. Sort of, kind of, probably not, probably just kidding. Myself. -- Another reason companies are spending billions is to defend their existing valuations. Google's value could go to zero if they don't keep up. Other companies likewise. It is the new high stakes ante for large informational/social service relevance.
- fsckboy 2y ago>The market needs to be irrational for you to stay solvent. it's a telling quote he chose: if you think AI is over invested, you should short AI companies, and that's where the quote comes from, problem is, even if you're right the market can stay irrational longer than you can afford to hold your short.
- ikrenji 2y agobe better of lighting the money on fire than shorting AI companies rn.
- phreeza 2y agoOne thing I have found myself wondering is why this didn't play out similarly with Google in the early days. I guess their secret sauce was just so good and so secret that neither established players nor copycat startups were able to replicate it, the way it happened with ChatGPT? Why is the same not the case here, is it just because the whole LLM thing grew out of a relatively open research culture where the fundamentals are widely known? OTOH PageRank was also published before the founding of Google. I'd be curious to hear if anyone has theories or insight here.
- isoprophlex 2y agoBeing "data driven" wasn't much of a thing yet back then. I mean, of course, there was massive energy around the web... but your average non-web corpo still had no idea. Of course there was pets.com or whatever dumb dotcom bubble shit, but I think noone saw data and digitalization as an absolutely essential part of making profits, or at least grabbing money, like we do now. Conversely, the 2020s has execs and other rich individuals doomscrolling LinkedIn and thinking that if they don't invest in the latest crypto/quantum/genai crap, they're missing out on a vital element of retaining competitive edge.
- bjornsing 2y agoThere were no cloud services to buy and no free open source software infrastructure, so the CAPEX for running PageRank on the whole Internet was huge.
- scotty79 2y ago> you probably don’t want to stake your business on always being the first company to find the next breakthrough that's like a better half of the entire Apple business model that brought them success, find the next hot thing (like a capacitive touchscreen or high density displays), make exclusive deals with hardware providers so you can release a device using that new tech and feed on it for some time till the the upstream starts leaking tech left and right and you competition can finally catch up
- t43562 2y agoObviously there's a huge technical change waiting in the wings because we don't need billions of dollars to make a human. Nor does a human need hundreds of kilowatts of electricity to think.
- ManlyBread 2y agoI am wondering where exactly are the AI products. I am not talking about boring, nerd stuff but rather about products that are well-known (to the point where non-technical users have heard of them) and are used on a daily basis by a significant amount of people. Currently only ChatGPT fits this bill despite the fact that the "AI revolution" is nearly two years old at this point.
- vouaobrasil 2y agoWell, hype up trash products on the iOS app store for example. Search AI there. Hundreds of useless things.
- n_ary 2y ago> I am wondering where exactly are the AI products ChatGPT is most popular and often in news, because it is the first of its kind(after siri/cortana) which was accessible to general public. I think, I have seen at least 100+ indie and commercial wrappers/apps offering access to better chat interface and common place to access various models from OAI/Anthropic/Mistral. At least one business I read about this weekend uses OAI API to ingest and summarize extensive mountain of documents to simplify some sort of regulatory certification process for medical device manufacturers. There was another one, which allowed a virtual assistant with basic activity ability(book appointments, write emails, transcribe phone calls, summarize letters etc.) Things will come eventually, as more and more people get access to the APIs and access becomes cheap(current model of per-token price is still immensely expensive imho) and general people do not understand what context is, what embedding is, what exactly is a model, what is an API. AI was/is being used massively in medical sector, defense/military, fraud detection(finance) and various other sectors for decades or more. Unfortunately, those applications are behind closed door and do not need to drum up public interest or investments directly as those are already well financed and usually referred to as ML/DL/CV etc. For example, I was doing an internship many many years back where my employer was using "AI"(though it was not referred to as such) to combine vision, noise, vibration, pressure and hundreds of other data to detect hit-n-run, break-in crimes, structural damage, prediction of collapse of large structures etc. and these were prominently used by insurance as well as some public service offices. These were specialized things, and outside of their closed doors, no one was interested about these.
- insane_dreamer 2y agoApple might emerge as one of the winners here, despite being one of the last to come out with an LLM, because it already has the infrastructure, or delivery system, for hundreds of millions of users to interact with an LLM. Google has a similar foothold worldwide, but its major weakness that it's cannibilizing its Search cash cow (how do you make $ from ads with LLMs?), whereas in Apple's case it enhances its product (finally a smart Siri). OpenAI doesn't have that except through Microsoft.
- teractiveodular 2y agoLLMs only cannibalize search if people use it as a replacement. I'm not seeing this happening at scale, instead they can be used to complement it by providing summaries, doing translations etc.
- insane_dreamer 2y agoIf people no longer click on sponsored links because they are getting a reply to their query in the summary paragraph at the top, Google no longer makes money from search. That's the cannibalization.
- mumblemumble 2y agoBut do I even want an actually smart Siri? Microsoft's been trying to ram essentially that down my throat for the better part of a year now, and it's mostly convinced me that the answer is "no". I don't want to have arbitrary conversations with my computer. I still just want the same thing I've been wanting from my digital assistant for 30 years now: fewer "eat up Martha" moments, and handling more intents so that I can ask "When does the next east-bound bus come?" and it stops answering questions like "Will it rain today?" as if I had asked "Is it raining right now?". None of those are particularly appropriate problems for a GPT-style model.
- PodgieTar 2y agoI would like to maybe be able to say: Set two timers, one for 20 minutes, and another for 50 minutes. or Turn off the lights in my living room, and my office. That's about as advanced as I want my home assistants to be though.
- insane_dreamer 2y agoThe biggest problem that I have with AI is how people extrapolate out of thin air, going from "LLMs can craft responses that sound exactly like a human" to "LLMs can solve climate change and world hunger". Those are two orthogonal skills and nothing we've seen so far indicates that LLMs would be able to make that jump, any more than I expect someone with a PhD in linguistics to solve problems faced by someone with a PhD in applied physics working on solving nuclear fusion.
- mumblemumble 2y agoClarke's Third Law is, has always been, and always will be the best explanation for how futurists think about these kinds of things.
- kombookcha 2y agoYeah, the issue with climate change and world hunger has never been the ability to generate loads and loads of vaguely relevant text in a very short time. It also isn't a problem of being able to recognize patterns in data, as you would with machine learning. They're issues of socially and politically organizing how humans and resources and economies interact, and there is nothing about the current crop of AI that suggests they can help with that in any particular capacity. If anything the genAI hunger for energy and chips is kinda looking like a net negative in terms of the first problem. I don't even really get why the hypemen are pushing these huge global issues as motivators - surely that insane standard of Unrealised Value only highlights how limited the abilities of this technology actually are?
- insane_dreamer 2y ago> I don't even really get why the hypemen are pushing these huge global issues as motivators - surely that insane standard of Unrealised Value only highlights how limited the abilities of this technology actually are? Sam Altman is either seriously drunk on his own success, or he's a snake-oil salesman: https://ia.samaltman.com/ https://ia.samaltman.com/ (Could be both; they are not mutually exclusive)
- _heimdall 2y ago
- iamgopal 2y agoAre we increasing or decreasing the "knowledge" entropy ? ( in the closed system of earth ? ) by doing AI ?
- steveBK123 2y agoI do wonder if they become another category like voice assistants where people just expect to get them free as part of an existing ecosystem. Or search/social media where people are happy to pay $0 to use it in exchange for ads. Sure some people are paying now, but its nowhere near the cost of operating these models, let alone developing them. Also the economics may not accrue to the parts of the stack people think. What if the model is commodity and the real benefits accrue to the GOOG/AAPL/MSFT of the world that integrate models, or to the orgs that gatekept their proprietary data properly and now can charge for querying it?
- mistercow 2y ago> If a competitor puts out a better model than yours, people can switch to theirs by updating a few lines of code. This may become increasingly the case as models get smarter, but it’s often not the case right now. It’s more likely to be a few lines of code, a bunch of testing, and then a bunch of prompt tweaking iterations. Even within a vendor and model name, it’s a good idea to lock to a specific version so that you don’t wake up to a bunch of surprise breakages when the next version has different quirks.
- trash_cat 2y agoDid Apple work when they launched the iPhone without the App store? It's a very similar question. There is this obsession to talk about tangible business value (and that its nowehere to be seen) despite OpenAI setting record for daily users. Right now it is a consumer product and will take time before we understand how to organize business around them. It took us about 20 years to get from the hype of .com bubble to tech giants.
- ozozozd 2y agoA very long-winded of saying LLMs are a commoditized offering despite being so new.
- ozozozd 2y agoA very long-winded of saying “LLMs are a commoditized offering despite being so new.”