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> Here is the state of venture capital in early 2025: Venture capital is moribund except AI. AI is moribund except OpenAI. OpenAI is a weird scam that wants to

• bobxmax 1y ago
  [flagged]
  • dang 1y ago
    I get the frustration of encountering shallow dismissals and curmudgeonly comments (this is why we changed the guidelines recently - https://news.ycombinator.com/item?id=43922742 https://news.ycombinator.com/item?id=43922742). At the same time, please don't react by breaking the site guidelines yourself or by putting down the community. It doesn't help; it only makes things worse. If you know more than others do, or have something more constructive to say, that's great! Please share some of that so the rest of us can learn and converse. If you don't want to or don't have time, that's also fine, but please don't add putdowns to the thread. It's always an option to downvote and/or flag comments that are breaking the rules, but if another commenter is wrong and you don't want to post a constructive correction, it's best to just move on. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
• gcanyon 1y ago
  > ChatGPT 4.5 today is still the same as it was with GPT-3/GPT-4 in 2022. 4.5/o3 doesn't seem hugely more intelligent then 3.0 I think you're misremembering how 3.0 worked. Granted, the slope from 2.0 to 3.0 was very steep, but a ton of progress has happened in the past few years.
  • averageRoyalty 1y ago
    Agreed, and as someone who's used it for work most days since 3.0 launch, it's likely way more efficient in outcomes - maybe 30-40%. But as the GP said, there's no paradigm shift. All the new features feel very gimicky, and OpenAI lost their first mover advantage quite a while ago.
    • stavros 1y ago
      Have you guys seen Deep Research? I'm completely blown away that I can have the thing make entire trip itineraries for me, something I just could not do before without paying a human lots of money.
      • technol0gic 1y ago
        halkias is that you? :P
      • hatefulmoron 1y ago
        I've used the deep research features from OpenAI, Grok, and most recently Anthropic. They're super impressive, but I can't shake the feeling that they're mostly giving me a summarized explanation of what the first 2 pages of Google Search is returning. Also, insofar that they're researching a topic, they're not sufficiently critical. They are highly influenced by what they read, and they tend to take the results they're given more or less at face value. For instance, I asked about a medical product for my girlfriend. I explicitly asked for a critical look at its efficacy, any studies in the area, etc.. and it seemed like 90% of the sources it considered were from the product's own website. It basically gave me an uncritical reporting of what they said about their own product.
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• wouldbecouldbe 1y ago
  But that’s normally how innovation goes. There is a big jump, and then a lot of margin work. People are expecting it to get exponentially better, but these kind of innovations are more a inversed power law.
  • TuringTest 1y ago
    True, but the religion of the Singularity that fueled this round of financing was premised on this improvement growing exponentially fast, thanks to the support provided by the current version of AI tools. There's no sign this will happen anytime soon.
  • exitb 1y ago
    Which would be fine if the market wouldn’t already price in the exponential growth.
  • betterThanTexas 1y ago
    I think you mean "invention". Innovation describes how products change over time and doesn't necessarily imply insight or value-adds. Sometimes it just means the packaging gets updated.
  • pier25 1y ago
    Most technology doesn't get exponentially better. We look at CPUs or the transmission of digital data and these seem to have improved exponentially but these are rather exceptions and are composed of multiple technologies at different stages. Like how we went from internet through phone lines, to dedicated copper lines for data, to optic fiber straight into to people's homes. Eg: look how the efficiency of solar cells has progressed over the last 50 years https://www.nrel.gov/pv/interactive-cell-efficiency https://www.nrel.gov/pv/interactive-cell-efficiency
  • asadotzler 1y ago
    The global broadband network that is the physical internet took about 10 years and $1T to build, mostly between 1998 and 2008 and it made the internet massively better every year of the build-out. That's also precisely as much time and money as has been put into this generative AI bubble. The internet was adding a trillion dollars to the global economy by 2008 and the end of that rapid expansion, where AI is still sucking hundreds of billions a year into a black hole with no killer use cases that could possibly pay off its investment, much less begin adding trillions to the global economy. And a decade before the web and internet explosion, PCs were similar, with a massive build out and immediate massive returns. This excuse making for AI is getting old. It needs to put up or shut up because so far it's a joke compared to real advances like the PC and the Internet, all while being hyped by VC collecting companies as the arrival of a literal God.
• Nauxuron 1y ago
  > 4.5/o3 doesn't seem hugely more intelligent then 3.0 -- it hallucinates less [...] This is not entirely true, or at least the trend is not necessarily less hallucination. See section 3.3 in the OpenAI o3 and o4-mini System Card[1], which shows that o3 and o4-mini both hallucinate more than o1. See also [2] for more data on hallucinations. [1]: https://openai.com/index/o3-o4-mini-system-card/ https://openai.com/index/o3-o4-mini-system-card/ [2]: https://github.com/vectara/hallucination-leaderboard/ https://github.com/vectara/hallucination-leaderboard/
• raincole 1y ago
  Saying today's SOTA models feel the same as GPT-3 did is the hot take of the year.
• IlikeKitties 1y ago
  I must say I do feel the newest GPT Versions are vastly better than the old ones. I found GPT-3 Stuff to be an interesting toy but too often it was too wrong and too stubborn to be useful. I use the 4.0+ Version regularly know. Just recently I took a screenshot from a jira Burndown chart to write a description of the sprint progress for our stakeholders. Did it in one shot from a screenshot and got it right.
• surgical_fire 1y ago
  This is sort of why I say that the hype is ultimately detrimental to the healthy development of tech. Generative AI was a sort of paradigm shift, and can be developed into interesting tools that boost human productivity. But those things take time, sometimes decades to reach maturity. That is not good for the get rich quick machine of Venture Capital and Hustle Culture, where quick exits require a bunch of bag holders. You gotta have suckers, and for that Gen AI cannot be an "interesting technology" with good potential. It needs to be "the future", that will disrupt everything and everyone.
• pzo 1y ago
  I think it's simplification to compare progress only on LLM level. We had big progress in AI in last 2 years but have to take into account more than text token generation. We have image generation that is not only super realistic but you just text what you want to modify without learning complicated tools like ComfyUI. We have text to speech and audio to audio that is not only very realistic and fluent with many languages but also can express emotions in speech. We have video generation that is really more realistic every month and taking less computation. There is big progress in 3d models generation. Speech to text is still getting improved and fast enough to run on phones reducing latency. Next frontier is how AI is applied for robotics. No to mention areas not sexy to end users but in application in healthcare.
  • lynx97 1y ago
    I have a similar feeling. While LLMs have given me a new way to do search/questions, it is the byproducts that feel like the actual game changers. For me, it is vision models and pretty impressive STT and TTS. I am blind, so I have my own reasons why Vision and Speech have so many real world applications for me. Sure, LLMs are still the backbone of the applications emerging, but the real progress in terms of use cases is in the fringes. Heck, I wrote myself my own personal radio moderator in a few hundred lines of shell, later rewritten in Python. As a simple MPD client. Watch out for a queued track which has albumart, and pass the track metadata + picture to the LLM. Send the result through a pretty natural sounding TTS, and queue the resulting sound file before the next track. Suddenly, I had a radio moderator that would narrate album art for me. It gave me a glimpse into a world that wouldn't have been possible before. And while the LLM is basically writing the script, the real magic comes from multimodal and great sounding TTS. Much potential for really cool looking/sounding PoCs. However, what makes me worry is that there is not much progress on (to me) obvious shortcomings. For instance, OpenAI TTS really can't speak any numbers correctly. Digits maybe, but once you hand it something like "2025" the chance is high it will have pronounciation problems. In the first months, this felt like bad but temporarily acceptable. A year later, it feels like hilariously sad that nothing has been done to address such a simple yet important issue. You know that something bad is going on when you start to consider expanding numbers to written-out form before passing the message to the TTS. My girlfriend keeps joking that since LLMs, we now have computers that totally can not compute correctly. And she has a point. Sure, hand the LLM a tool to do calculations, and the situation improves somewhat. But it seems to be underlying, as shown by the problems of TTS. Vision models have so many applications for me... However, some of them turn out to be actually unusable in practice. That becomes clear when you use a vision model to read the values off a blood pressure sensor. Take three photos, and you get three slightly different values. Not obviously made up stuff, but numbers that could be. 145/90, 147/93, 142/97. Well, the range might be clear, but actually, you can never be sure. Great for scene and art descriptions, since hallucinations almost fall through the cracks. But I would never use it to read any kind of data, neither OCR'd text nor, gasp, numbers! You can never know if you have been lied to. But still, some of the byproducts of LLMs feel like a real revolution. The moment you realize why whisper is named like that. When you test it on your laptop, and realize that it just transcribed the YouTube video you were rather silently running in the background. Some of this stuff feels like a big jump.
• rafram 1y ago
  I totally disagree. The initial release of ChatGPT felt magical because of how cool was to be able to hold a natural, fluid conversation with a computer. But it was an extremely stupid computer beneath that veneer of conversational intelligence. Recent models can do a lot of math accurately without “reasoning” or invoking tools; GPT-3 could barely perform basic arithmetic.
  • spunker540 1y ago
    I agree with your take, but at the same time, I’m not sure these things should ever do math. I know that they can, but it seems impossible to draw the line of math they can do vs math they shouldn’t do. A part of me suspects they should always be outsourcing any math to a tool.
    • rafram 1y ago
      Absolutely, but the ability to perform complex math in one step seems like a good indicator that it isn’t just a convincing conversational autocomplete anymore.
      • jcgrillo 1y ago
        But is it anything more than a convincing mathematical autocomplete? Or a convincing code autocomplete? Mathematics and code are themselves primarily conversational tools, with side effects.
  • andrepd 1y ago
    > Recent models can do a lot of math accurately without “reasoning” or invoking tools; They most certainly cannot
  • asadotzler 1y ago
    And the latest models get beat on reliability by a $2.99 drug store slim wallet sized solar powered calculator you could have bought 35 years ago for about that same price.
• Xenoamorphous 1y ago
  > 4.5/o3 doesn't seem hugely more intelligent then 3.0 I wonder what “hugely more intelligent” would look like to you? Also, in the (rather short) history of computing, what “hugely more X” has happened over the course of a couple of years?
  • OJFord 1y ago
    Uh, compute power? Storage?
  • fragmede 1y ago
    I think it’s worth mentioning that OpenAI was founded in 2015, so ChatGPT's overnight success in 2022 was 7 years in the making. Looking at that timescale, I'd say Google counts, as do several other companies is we look at inception till successful product.
• deleted 1y ago
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• drob518 1y ago
  Yea it hallucinates less, but it still hallucinates a lot. I think we’re proving that intelligence is not just a language model, even an obscenely large one.
  • forgetfreeman 1y ago
    I'm waiting impatiently for someone to connect the obvious dots and roll out an AI assistant intentionally patterned after Hunter S Thompson. The hallucinations are now a feature.
  • thatguy0900 1y ago
    I mean, if you look up the data around eye witness testimony being extremely unreliable even for people who directly witnessed things, it's not out of the realm that humans regularly hallucinate as well
  • asadotzler 1y ago
    It's actually hallucinating more. The more they mess with these now mature models, models which no longer scale with training corpus size, the more they push and prod at the models to do better with chain of thought and other techniques, the more they hallucinate. It's getting worse because the magic of LLM scaling is over and the techniques we have to make them better actually make the hallucinations worse.
• calebkaiser 1y ago
  I understand the spirit in this line of criticism, but I think it's easy to muddle the timelines and feel as if things "aren't moving," when in fact, the pace of research and improvement is great. For context: - GPT 2 was released in Feb 2019 - GPT 3 came out roughly 18 months later in 2020. It was a huge jump, but still not "usable" for many things. - InstructGPT came out roughly 18 months later in early 2022, and was a huge advancement. This is RLHF's big moment. - About 10 months later, ChatGPT is released at the end of 2022 as a "sibling" to InstructGPT. It's an "open research preview" at this point. This is around the time OpenAI starts referring to certain models as being in the "3.5 family" - GPT-4 comes out in March 2023, so barely 2 years ago now. Huge jumps in performance, context window size, and it supports images. This is around the time ChatGPT hits 100 million users and is really becoming a reliable, widely adopted tool. This is also the same time that tools like Cursor are hitting the market, though they haven't exploded yet. Models are just now getting "good enough" for these kinds of applications - GPT-4-Turbo comes out in November 2023, with way larger context windows and lower pricing. - About 12 months ago, GPT-4o released, showing slightly increased performance on existing benchmarks over 4, but now with state-of-the-art audio capability support for something like 50 languages. - 5 months ago, o1 releases. This is a big moment for scaling compute at test time, which is a major current research direction in ML. Shows huge improvements (something like 8x over 4) on some math/reasoning benchmarks. Within months, we have o3 and o4, which substantially improve these scores even further. - In February of this year, we get 4.5, and then months later, the confusingly named 4.1, which shows improvements over 4o. So to be clear, in 2019 we had an interesting research project that only a few people could tinker with. 18 months later, we had a better model that you could play with via an API, but was still a toy. It takes more than two years to go from that to ChatGPT, and a few more months (nearly 3 years total) to get to the "useful" version of ChatGPT that really sets the world on fire. It took roughly 4 + 1/2 years to go from "novelty text generation" to "useful text generation". In the 2 years since then, we've gotten multimodal models, a new class of reasoning models, baseline improvement across performance, and more. If anything, there is more fundamental research and wider variety of directions now (the kind of stuff that shifts paradigms) than before.
  • bigstrat2003 1y ago
    I don't agree. I think it's still just a toy that can't get anything useful done. It still hallucinates quite a bit, and gives confidently wrong answers all the time. It's impressive that we could make a program that does all those things, but until they get the accuracy to a reasonable level it won't be useful.
• Traubenfuchs 1y ago
  + one thing I just absolutely can‘t understand: What‘s their moat? Their offerings are replaceable by like 5 direct competitors, some of which are open source. Pricewise, it‘s a race to the bottom, and the bottom is „api pricing vs running open source yourself“.
  • deleted 1y ago
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• nopinsight 1y ago
  > 4.5/o3 doesn't seem hugely more intelligent then 3.0 I disagree with 3.0, but perhaps that feels true for 4.0 or even 3.5 for some queries. The reason is that when LLMs are asked questions whose answers can be interpolated or retrieved from their training data, they will likely use widely accepted human knowledge or patterns to compose their responses. (This is a simplification of how LLMs work, just to illustrate the key point here.) This knowledge has been refined and has evolved through decades of human experiments and experiences. Domain experts of varying intelligence will likely come up with similar replies on these largely routine questions as well. The difference shows up when you pose a query that demands deep reasoning or requires expertise in multiple fields. Then, frontier reasoning models like o3 can sometimes form creative solutions that are not textbook answers. I strongly suspect that Reinforcement Learning with feedback from high-quality simulations or real environments will be key for these models' capabilities to surpass those of human experts. Superhuman milestones, equivalent to those achieved by AlphaGo and AlphaZero between 2016 and 2018, might be reached in several fields over the coming years. This will likely happen first in fields with rapid feedback loops and highly accurate simulators, e.g. math problem solving (as opposed to novel mathematical research), coding (as opposed to product innovation).
• brundolf 1y ago
  Agreed. It's become a pretty useful tool for individual people to use for individual tasks. But to hyper-scale to a point where it justifies crazy valuations - even short of super-intelligence - it has to scale beyond users. It has to no longer require human oversight from one step to the next. I think that's the key threshold all these companies have been running up against, and crossing it would be the paradigm shift we keep hearing about. But they've been trying for years, and haven't done it yet, and seem to be plateauing And then in OpenAI's case specifically- this tech has become commoditized really quickly. They have several direct competitors, including a few that are open, and their only real moat is their brand and Sam's fundraising ability. Their UX is one of the best right now, but that isn't really a moat
  • HDThoreaun 1y ago
    > it has to scale beyond users. What? MSFT is worth 3.25 trillion without scaling beyond any users. All these AI companies need is for everyone to pay them $100 a year for a subscription and they have more than justified their valuation. Same strategy Microsoft uses
• jsnell 1y ago
  > The interesting thing, to me, is how speculative OpenAI's bet is. It doesn't really matter how speculative the AGI bet is, their consumer AI business by itself is basically guaranteed to drown them in money. The only reason they're making losses at the moment is because they're choosing not to monetize their free tier users with ads, presumably since they don't need to make a profit and can prioritize growth. But the moment they flip the advertising switch, their traffic will be both highly monetizable and ludicrously high margin.
  • KerrAvon 1y ago
    Maybe, but: 1. People won’t ultimate go download ChatGPT.app or use a website — they’re going to be using the functionality through structured services in iOS and Android, and it’s necessarily going to be under control of the OS vendors for security/privacy reasons. This doesn’t mean Apple and Google own the LLMs — there will be consumer choice for antitrust reasons if nothing else — but the operating system has to be a conduit for access to your data, and also for unified user experience. Which means advertising will be limited. 2. Say it does go the way you think it will — what prevents a real non-profit, open source LLM from taking it away from the commercial players? There really is no moat (other than money, energy, data center space).
  • asadotzler 1y ago
    Not really. The best math I've seen says OAI cannot break even with ads on free tiers without about 5X as many users. That's to break even, not to be wildly profitable as you seem to be suggesting. OAI will need more than ChatGPT, pro and "free" to be anything close to web search for ad revenue.
    • jsnell 1y ago
      It'd be easier to address said "best math you've seen" if you could provide a link to that analysis... I hope it wasn't by Ed Zitron. But it seems pretty obvious that the math must be based on some incorrect assumptions. The unit inference costs of high quality LLMs are much lower than the unit costs of serving high quality web search queries. The LLM costs have also been decreasing rapidly -- 1000x in two years seems like a fair estimate -- while search engine costs haven't. (If anything they've been going up.) And web search is obviously a business that is very profitable with the ad model, despite those higher unit costs. > OAI will need more than ChatGPT, pro and "free" to be anything close to web search for ad revenue. I don't understand this part at all. Revenue is proportional to traffic and ad rates. Why would the ad rates be lower for chatbots? Or why would their traffic obviously be lower? This is also moving the goalposts. Search ad revenue is what, a $250B/year market. OpenAI doesn't anywhere near that much revenue to be fabulously profitable. A tenth of it would already be more than enough.
• ctoth 1y ago
  Here's a thing you can do right now, today. - Go down to your local Ray-Ban store. - Ask to play with a pear of the Meta glasses. - Activate "Live AI mode" - Have a real time video conversation with an AI which can see what you see, translate between languages, read text, recognize objects, and interact with the real world. Contrary to your (potentially misremembered?) history, nothing at all like this was possible in 2019. I remember finetuning an early GPT-2 (before they even released the 2B model!) on a large corpus of Star Wars novels and being impressed that it would mention "Luke" when I ran the produced model! Now I wear it on my head and read restaurant menus with it. Use it to find my Uber (what kind of car is that?) Today I am building my raised garden beds out back and reading the various soil amendments I purchased, talking about how much bloodmeal to put over the hugelkultur layer, having it do math, and generally having a pair of eyeballs. I'm blind. The amount of utility I get out of these things is ... very hard to overstate. If this's "moribund," sign me up for more decay.
  • amazingamazing 1y ago
    Hasn’t Google lens been doing that for almost a decade now?
    • acchow 1y ago
      Like how Google Translate has been translating between languages for a decade+ now...? There's been a stepwise jump in the capabilities of AI that's changed "products" from mostly fun to actually useful
      • amazingamazing 1y ago
        Are you implying that google translate was not useful to anyone prior to chat gpt3?
  • benatkin 1y ago
    The Facebook glasses aren't as relevant as the LLMs doing many thinking tasks that people get paid to do. A lot of it is as a productivity multiplier, and I'm not saying it's all doom and gloom, but it's transformative.
• nine_k 1y ago
  LLMs are in the limelight, but I won't dismiss the notable progress in areas like vision and visual parsing, and also image synthesis and transformation. Robotic taxis finally are running down city streets, and drive on par with humans, or better. You can give the machine a rough sketch, and get a well-made picture. You can show the machine a few photos, and get a very reasonable 3D model, even in a form of reasonable meshes. Etc.
  • asadotzler 1y ago
    All of that was progressing well before LLMs and will be afterwards too because it has more practical value even if less "OMG WOW" factor.
• insane_dreamer 1y ago
  > it hallucinates less, does it? An anecdote from yesterday: My wife was asked to do a lit review as an assignment for nursing school. Her professor sent her an example of papers on the topic, with a brief "relevance" summary for each. My wife asked me for help as she was frustrated she couldn't find any of the referenced papers online (she's not the most adept at technology and figured she was doing something wrong). I took one look at the email from her professor and could tell just by the formatting that it was LLM generated (which model, I don't know, but obviously a 2025 model). The professor didn't say anything about using an LLM, and my wife didn't suspect that might be the case. My wife and I did some Google Scholar searches, and _every_ _single_ _one_ of the 5 papers cited did _not_ exist. In 2 of the cases, similar papers did exist, but with different authors or a different title that resembled the fake "citation". The three others did not exist in any form - there were other papers on the same subject, sure, but nothing closely resembling the "citations" either in terms of authorship or title.