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AI founders will learn the bitter lesson

• mana7272 2y ago
  love this
• ano-ther 2y ago
  So what is the appropriate course of action if you are a founder? Just wait until the models inevitably get better, or help a customer with their problem now? Computer power has been increasing all the time, but that hasn't kept people from experimenting with the limited power of their time (which ultimately led to better solutions) rather than waiting for the more powerful machines.
  • dsr_ 2y ago
    Find a real problem and solve it cheaply, or solve it so much better than anyone else that your high price is worthwhile to enough people. Don't focus on the method of solving the problem until you have identified the problem and all the current solutions and avoidances.
  • amelius 2y ago
    You can help the customer now, but you should keep in mind that the people working on the more general solutions will at some point take your moat. E.g. you can build a drawing program where you can tell it in natural language to apply certain effects and make some changes. But another company might be working on a OS assistant that can help the user in any program currently running on the computer. Or you can build a robot that can pick bolts from an assembly line, but at some point there will be a company building a cheap robot that can do anything from folding t-shirts to knitting a sweater.
    • zol 2y ago
      This was my takeaway from the article too.
• keybored 2y ago
  > “The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin”. > He points out that throughout AI’s history, researchers have repeatedly tried to improve systems by building in human domain knowledge. The “bitter” part of the title comes from what happens next: systems that simply use more computing power end up outperforming these carefully crafted solutions. We’ve seen this pattern in speech recognition, computer chess, and computer vision. If Sutton wrote his essay today, he’d likely add generative AI to that list. And he warns us: this pattern isn’t finished playing out. According to Chomsky (recalled in relatively recent years for him) this is why he didn’t want to work on the AI/linguistics intersection when someone asked him in the mid 50’s. Because he thought that successful approaches would just use machine learning and have nothing to do with linguistics (that he cares about).
  • ajuc 2y ago
    Seems intellectually dishonest (given how his theory of universal grammar is all about indispensible biological factors in human languages).
• CaptainFever 2y ago
  Cool article, a lot more technical and informational than I thought from the headline. The article gave the TL;DR as below, for those who skip to the comments: Historically, general approaches always win in AI. Founders in AI application space now repeat the mistakes AI researchers made in the past. Better AI models will enable general purpose AI applications. At the same time, the added value of the software around the AI model will diminish.
• doctorpangloss 2y ago
  More computation cannot improve the quality or domain of data. Maybe the bitter lesson lesson is, lobby bitterly, for copyright laws that favor what you are doing, and weakened anti trust, to give you the insurmountable moat of exclusive data in a walled garden media network.
  • guax 2y ago
    A human does not need billions of driving hours to learn how to drive competently. The issue with current method is not quality of data but methodology. More computation might unlock newer approaches that are better with less and worse quality data.
    • Zr01 2y ago
      A human is not a blank slate. There's millennia of evolutionary history that goes into making a brain adapted and capable of learning from its environment.
      • qeternity 2y ago
        A human is a mostly blank slate...but it's a really sophisticated slate that as you say has taken many millions of years of development.
    • namaria 2y ago
      I think there's a more fundamental problem at play here: what seems to work in 'AI', search, is made better by throwing more data into more compute. You then store the results in a model, that amounts to pre-computed solutions waiting for a problem. Interacting with the model is then asking questions and getting answers that hopefully fit your needs. So, what we're doing on the whole seems to be a lot of coding and decoding, hoping that the data used in training can be adequately mapped to the problem domain realities. That would mean that the model you end up with is somehow a valid representation of some form of knowledge about the problem domain. Trouble is, more text won't yield higher and higher resolution of some representation of the problem domain. After some point, you start to introduce noise.
• kubb 2y ago
  They will learn their lesson how exactly? By getting money for free from VCs? Remember, most of these startups are grifters. Only few of them really believe in their products.
• moomin 2y ago
  The thing is, what do we do with the bitter lesson once we’re essentially ingesting the entire internet? More computation runs the risk of overfitting and there just isn’t any more data. Is the bitter lesson here telling me that we’ve basically maxed out?
  • Filligree 2y ago
    We’re nowhere near ingesting the whole internet. Though personally, I think we’re missing whatever architecture / mathematical breakthrough will make online learning (or even offline incremental, I.e. dreams) work. At that point we could give the AI a robot body and train it of lived experience.
    • csmpltn 2y ago
      > "We’re nowhere near ingesting the whole internet." We don't need to ingest the whole internet. I'd wager that upwards of 75% of the internet is spam, which would be useless for LLM training purposes. By the way, spam and useless information on the internet is only going to get worse, largely thanks to LLMs. Only a subset of the internet contains "useful" information, an even a smaller subset contains information which is "clean enough" to be used for training purposes, and an even smaller subset can be legally scraped and used for training purposes. It's highly likely that we've reached "peak training data" a long time ago, for many areas of knowledge and activities which are available on the internet.
  • deleted 2y ago
    [deleted]
  • kouteiheika 2y ago
    > More computation runs the risk of overfitting and there just isn’t any more data. At this scale you can't overfit. The model might not improve in a meaningful way, but it can't overfit because the amount of data is much much larger that the size of the model. That said, as the state of the art open models show, the way to get better models is not "use more data", but use "more high quality data" (e.g. look at the graphs here comparing the datasets: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu). I don't know how much high quality data we can extract from the Internet (most of the data on the Internet is, as you'd guess, garbage, which is why aggressively filtering it can improve performance so much), but I'd wager we're still nowhere near running out. There's also a ton of data that's not available on the public Internet in an easily scrapable form, or even at all; e.g. Anna's Archive apparently contains almost 1PB of data, and even that is (by their estimate) only ~5% of the world's books.
• NameError 2y ago
  I think the core problem at hand for people trying to use AI in user-facing production systems is "how can we build a reliable system on top of an unreliable (but capable) model?". I don't think that's the same problem that AI researchers are facing, so I'm not sure it's sound to use "bitter lesson" reasoning to dismiss the need for software engineering outright and replace it with "wait for better models". The article sits on an assumption that if we just wait long enough, the unreliability of deep learning approaches to AI will just fade away and we'll have a full-on "drop-in remote worker". Is that a sound assumption?
• kopirgan 2y ago
  May be I'm being dumb but isn't the AI approach somewhat like brute force hacking of password? I mean humans don't learn that way - yes we do study code to be better coders but not in the way AI learning does? Will someone truly discover a way to start from fundamentals and do better not just crunch zillions of petabytes faster and faster. Or is that completely wrong? Happy to stand corrected
  • TylerLives 2y ago
    As long as people are not willing to acknowledge that we're not blank slates, but posses vast intelligence inherently, and that even the simplest life forms are infinitely more intelligent than LLMs, there won't be progress.
    • _heimdall 2y ago
      Is that something we can acknowledge without at least somewhat understanding how that works? It sure seems like we have a lot of inherent knowledge, but by the book we're little more than the product of an instruction set that is our DNA and there's no explanation that includes inherited knowledge in that DNA. I think the harder thing we need to acknowledge is that we understand much less than we think we know. Concepts like inherent, or collective, knowledge would all roll down hill from that.
    • kopirgan 2y ago
      I think the brilliant minds working on this know this. It shouldn't stop things just as we'll die one day doesn't stop accumulating wealth or working hard. But at some point I hope there's breakthrough from any entirely different paradigm.
  • _heimdall 2y ago
    That's part of the problem though, isn't it? We still don't really understand how human intelligence works. We don't know how we learn. We don't even know where or how memories are stored. We have ideas about how it works, sure. We know a bit about how the basics of the brain works and we know some correlations of what areas of the brain electrically light up in various conditions, but that's about the extent of it. Its hard to really create an artificial version of something without pretty well understanding the real thing. In the meantime, brute forcing it is probably the best (and most common) approach.
• CharlieDigital 2y ago
  There's only one core problem in AI worth solving for most startups building AI powered software: context. No matter how good the AI gets, it can't answer about what it doesn't know. It can't perform a process for which it doesn't know the steps or the rules. No LLM is going to know enough about some new drug in a pharma's pipeline, for example, because it doesn't know about the internal resources spread across multiple systems in an enterprise. (And if you've ever done a systems integration in any sufficiently large enterprise, you know that this is a "people problem" and usually not a technical problem). I think the startups that succeed will understand that it all comes down to classic ETL: identify the source data, understand how to navigate systems integration, pre-process and organize the knowledge, train or fine-tune a model or have the right retrieval model to provide the context. There's fundamentally no other way. AI is not magic; it can't know about trial ID 1354.006 except for what it was trained on and what it can search for. Even coding assistants like Cursor are really solving a problem of ETL/context and will always be. The code generation is the smaller part; getting it right requires providing the appropriate context.
  • osigurdson 2y ago
    Wouldn't this just be foundational model + RAG in the limit?
    • CharlieDigital 2y ago
      RAG is the action of retrieval to augment generation. Retrieval of what? From where? The process that feeds RAG is all about how you extract, transform, and load source data into the RAG database. Good RAG is the output of good ETL.
  • skrebbel 2y ago
    Yeah seems like context is the AI version of cache invalidation, in the sense of the joke that "there's only 2 hard problems in computer science, cache invalidation and naming things". It all boils down to that (that, and naming things)
    • 2y ago
• quantumgarbage 2y ago
  The mistake this post makes is thinking that sole engineering effort is what makes startup win. Most of the things you build will end up in the trash can anyway. But what matters is what you get while doing them, like domain knowledge, improved processes, actual customers.. If you are ok to scrap your code in order to integrate a better model then fine. Since your model is better and does not need all the software around it, it will take 10x less time to re-do what you have, leaving you more time for things that also matter, like sales.
• 9dev 2y ago
  Well. We were working on a search engine for industry suppliers since before the whole AI hype started (even applied to YC once), and hit a brick wall at some point were it got too hard to improve search result quality algorithmically. To understand what that means: We gathered lots of data points from different sources, tried to reconcile that into unified records, then find the best match for a given sourcing case based on that. But in a lot of cases, both the data wasn’t accurate enough to identify what a supplier was actually manufacturing, and the sourcing case itself wasn’t properly defined, because users found it too hard to come up with good keywords for their search. Then, LLMs entered the stage. Suddenly, we became able to both derive vastly better output from the data we got, and also offer our users easier ways to describe what they were looking for, find good keywords automatically, and actually deliver helpful results! This was only possible because AI augments our product well and really provides a benefit in that niche, something that would just not have been possible otherwise. If you plan on founding a company around AI, the best advice I can give you is to choose a problem that similarly benefits from AI, but does exist without it.
  • openrisk 2y ago
    > the data wasn’t accurate enough to identify what a supplier was actually manufacturing how did the LLM help with that challenge?
    • HeatrayEnjoyer 2y ago
      A guess: Their ability to infer reality from incomplete and indirect information.
• mercurialsolo 2y ago
  love it when a 25 year old founder says we have been here multiple times in the past
  • welder 2y ago
    s/25 year old founder/25 yr old first-time founder/
  • evujumenuk 2y ago
    In which specific ways are they wrong, though? Being able to learn from others' mistakes is generally considered to be a sign of aptitude.
    • danielmarkbruce 2y ago
      They picked the wrong analogy, they misunderstand how much human tuning via specific data sets which show how to arrive at an answer (sound familiar to feature engineering?), they misunderstand how specific vertical solutions get. Why not pick databases wrapped in a UI as an analogy? That analogy would go the other way - despite the fact it seems simple and sensible from an integration perspective to have a single database with all the UI a business would need, we have a bajillion specialist SAAS products which then jump through hoops to integrate with each other? Why? Because the workflows are so damned specific to the tasks at hand, some "generic" solution doesn't work.
• k__ 2y ago
  I'm not an AI founder, but I'd say there is still some value to be added by better architectures. These AI wrappers run ship engines on a kayak. For example, most coding companion's ignore my code base the moment I close other files. Somehow their context is minimal. Such systems can be improved dramatically just by changing the software around the LLM. But I get it, you have to be quick and don't waste time on MVPs. If you get critical mass, you can add that later... hopefully
• JohnCClarke 2y ago
  The smarter AI app founders know that more advanced AI will easily replace their current tech. What they're trying to do now is lock in a userbase.
• elisharobinson 2y ago
  this is not only not true , it has no basis's in reality. In the "real" world there are tradeoff's and constraints. scaling does not work infinitely , and products which are delivered by good engneering cultures have a non linear growth ( bad vs very good ). comming back to research one of the frontier model's deepseek was able to come close to SOTA with a relatively small budget because of one of their mixture of experts approach.
  • moffkalast 2y ago
    Yeah knowing that having infinite perfect data and infinite compute to train an end-to-end DNN is the way to solve anything is great, but not exactly helpful when you don't have either of those things. Not to mention having to actually deploy it on a low power system in the end.
• timabdulla 2y ago
  I think one thing ignored here is the value of UX. If a general AI model is a "drop-in remote worker", then UX matters not at all, of course. I would interact with such a system in the same way I would one of my colleagues and I would also give a high level of trust to such a system. If the system still requires human supervision or works to augment a human worker's work (rather than replace it), then a specific tailored user interface can be very valuable, even if the product is mostly just a wrapper of an off-the-shelf model. After all, many SaaS products could be built on top of a general CRM or ERP, yet we often find a vertical-focused UX has a lot to offer. You can see this in the AI space with a product like Julius. The article seems to assume that most of the value brought by AI startups right now is adding domain-specific reliability, but I think there's plenty of room to build great experiences atop general models that will bring enduring value. If and when we reach AGI (the drop-in remote worker referenced in the article), then I personally don't see how the vast majorities of companies - software and others - are relevant at all. That just seems like a different discussion, not one of business strategy.
  • ilaksh 2y ago
    I guess part of the point is that the value of the UX will quickly start to decrease as more tasks or parts of tasks can be done without close supervision. And that is subject to the capabilities of the models which continues to improve. I suggest that before we satisfy _everyone_'s definition of AGI, more and more people may decide we are there as their own job is automated. The UX at that point, maybe in 5 or 10 or X years, might be a 3d avatar that pops up in your room via mixed reality glasses, talks to you, and then just fires off instructions to a small army of agents on your behalf. Nvidia actually demoed something a little bit like that a few days ago. Except it lives on your computer screen and probably can't manage a lot of complex tasks on it's own. Yet. Or maybe at some point it doesn't need sub agents and can just accomplish all of the tasks on its own. Based on the bitter lesson, specialized agents are probably going to have a limited lifetime as well. But I think it's worth having the AGI discussion as part of this because it will be incremental. Personally, I feel we must be pretty close to AGI because Claude can do a lot of my programming for me. I still have to make important suggestions, and routinely for obvious things, but it is much better at me at filling in all the details and has much broader knowledge. And the models do keep getting more robust, so I seriously doubt that humans will be better programmers overall for much longer.
• DebtDeflation 2y ago
  >Eventually, you’ll just need to connect a model to a computer to solve most problems - no complex engineering required. The word "eventually" is doing a lot of work here. Yes, it's true in the abstract, but over what time horizon? We have to build products to solve today's problems with today's technology, not wait for the generalized model that can do everything but may be decades away.
  • timabdulla 2y ago
    Based on the author's company that be founded, I assume he believes this technology is just years away. I think with a lot of AI folk in San Francisco, this is a tacit assumption when having these sorts of conversations.
    • prmph 2y ago
      Anyone that thinks this is just years away is utterly ignoring human history, nature, and relationship with technology. My own view is that this will never be achieved, and it's not even just about the tech. Let's imagine for a moment that this is even achieved. Then, there is still complex engineering required in the world: to maintain and continually improve the AI engines and their interfaces. Unless you want to say that, past some point, the AI will be self-improving without any human input whatsoever. Unless the AI can read our minds, I'm not sure it can continue to serve human interests without human input. But, never mind, we will never get there. At this very moment, tech is capable of so much more, but most sites I visit have bad UI, are bloated downloading and executing massive amounts of JS, riddled with annoying ads that serve no real useful purpose to society, and riddled with bugs. Even as an engineer, I really struggle to find any good no-code tools to create anything truly sophisticated without digging into hard-core code. Heck, they are now talking about adding more HTTP methods to HTML forms.
  • amelius 2y ago
    True, but it tells that if you are a founder of a niche AI company then you should take money out of it instead of investing everything back into the company, because eventually the generalist-AI will destroy your business and you will be left with nothing.
• jonnycat 2y ago
  I think this argument only makes sense if you believe that AGI and/or unbounded AI agents are "right around the corner". For sure, we will progress in that direction, but when and if we truly get there–who knows? If you believe, as I do, that these things are a lot further off than some people assume, I think there's plenty of time to build a successful business solving domain-specific workflows in the meantime, and eventually adapting the product as more general technology becomes available. Let's say 25 years ago you had the idea to build a product that can now be solved more generally with LLMs–let's say a really effective spam filter. Even knowing what you know now, would it have been right at the time to say, "Nah, don't build that business, it will eventually be solved with some new technology?"
  • ilaksh 2y ago
    You think it will be 25 years before we have a drop in replacement for most office jobs? I think it will be less than 5 years. You seem to be assuming that the rapid progress in AI will suddenly stop. I think if you look at the history of compute, that is ridiculous. Making the models bigger or work more is making them smarter. Even if there is no progress in scaling memristors or any exotic new paradigm, high speed memory organized to localize data in frequently used neural circuits and photonic interconnects surely have multiple orders of magnitude of scaling gains in the next several years.
    • sealeck 2y ago
      I think you're suffering from some survivorship bias here. There are lot of technologies that don't work out.
      • ilaksh 2y ago
        Computation isn't one of them so far. Do you believe this is the end of computing efficiency improvements?
        • rsynnott 2y ago
          No, but there's really very little reason to think that that makes the ol' magic robots less shit in any sort of well-defined way. Like, it certainly _looks_ like they've plateaued. I often suspect that the tech industry's perception of reality is skewed by Moore's Law. Moore's Law is, quibbles aside, basically real, and has of course had a dramatic impact on the tech industry. But there is a tendency to assume that that sort of scaling is _natural_, and the norm, and should just be expected in _everything_. And, er, that is not the case. Moore's Law is _weird_.
• tinco 2y ago
  This might be true on a very long timescale, but that's not really relevant for VC's. Literally every single VC I've talked to raised the question if our moat is not just having better prompts, it's usually the first question. If a VC really invested in a company whose moat got evaporated by O1, that's on the VC. Everyone saw technology like O1 coming from a mile away. For the slightly more complex stuff, sure at some point some general AI will probably be able to do it. But with two big caveats, the first being: when? and the second being: for how much?. In theory every deep and wide enough neural network should be able to be trained to do object detection in images, yet no one is doing that. Technologies specifically designed to process images, like CNN's, reign supreme. Likewise for architectures of LLM's. At some point your specialization might become obsolete, but that point might be a decade or more from now. Until then, specializations will have large economic and performance advantages making the advancements in AI today available to the industry of tomorrow. I think it's the role of the VC to determine not if there's an AI breakthrough behind a startups technology, but if there's a market disruption and if that market disruption can be leveraged to establish a dominant company. Similar to how Google leveraged a small and easily replicable algorithmic advantage into becoming one of the most valuable companies on earth.
  • thegeomaster 2y ago
    On your object detection point, Gemini 2.0 Flash has bounding box detection: https://ai.google.dev/gemini-api/docs/models/gemini-v2#bounding-box https://ai.google.dev/gemini-api/docs/models/gemini-v2#bound.... I haven't found it to work particularly well for some more domain-specific things I tried, but it was surprisingly good for an LLM.
• mike_hearn 2y ago
  Of course, 01 is a lot more expensive than smaller models. So the startups that have spent time on tuning their prompts May yet get the last laugh, at least in any competitive market where customers are price sensitive. Bear in mind that supposedly OpenAI is losing money even at a $200 a month price point so it's unclear that the current cost structure of model access is genuinely sustainable.
• diziet_sma 2y ago
  > We’ve seen this pattern in speech recognition, computer chess, and computer vision. I think the bitter lesson is true for AI researchers, but OP overstates it's relevance to actual products. For example the best chess engines are very much still very much specific to chess. They incorporate more neural networks now, but they are still quite specific.
  • dosinga 2y ago
    This. ChatGPT doesn't play chess very well.
• resiros 2y ago
  The author discusses the problem from the point of engineering, not from business. When you look at it from business perspective, there is a big advantage of not waiting, and using whatever exists right now to solve the business problem, so that you can get traction, get funding, grab marketshare, build a team, and when the next day a better model will come, you can rewrite your code, and you would be in a much better position to leverage whatever new capabilities the new models provide; you know your users, you have the funds, you built the right UX... The best strategy from your experience, is to jump on a problem as soon there is opportunity to solve it and generate lots of business value within the next 6 months. The trick is finding that subproblem that is worth a lot right now and could not be resolved 6 months ago. A couple of AI-sales startups "succeeded" quite well doing that (e.g. 11x), now they are in a good position to build from there (whether they will succeed in building a unicorn, that's another question, it just looks like they are in a good position now).
  • ripped_britches 2y ago
    Very true. Most code written today will probably be obsolete in 2050. So why write it? Because it puts you in a good strategic position to keep leading in your space.
• jillesvangurp 2y ago
  I think this is largely right. I look a this space as somebody that plugs together bits and pieces of software components for a living for a few decades. I don't need to deeply understand how each of those things work to be effective. I just need to know how to use them. From that point of view, AI is more of the same. I've done a few things with the OpenAI apis. Easy work. There's not much to it. Scarily simple actually. With the right tools and frameworks we are talking a few lines of code mostly. The rest is just the usual window dressing you need to turn that into an app or service. And LLMs can generate a lot of that these days. The worry for VC funded companies in this space is that a lot of stuff is becoming a commodity. For example, the llama and phi models are pretty decent. And you can run them yourself. Claude and OpenAI are a bit better and larger so you can't run them yourself. But increasingly those cheap models that you can run yourself are actually good enough for a lot of things. Model quality is a really hard to defend moat long term. Mostly the advantage is temporary. And most use cases don't actually need a best in class LLM. So, I'm not a believer in the classic winner takes all approach here where one company turns into this trillion dollar behemoth and the rest of the industry pays the tax to that one company in perpetuity. I don't see that happening. The reality already is that the richest company in this space is selling hardware, not models. Nvidia has a nice (temporary) moat. The point of selling hardware is that you want many customers. Not just a few. And training requires more hardware than inference. So, Nvidia is rich because there are a lot of companies busy training models.
  • crypto420 2y ago
    > So, I'm not a believer in the classic winner takes all approach here where one company turns into this trillion dollar behemoth and the rest of the industry pays the tax to that one company in perpetuity. I agree with this sentiment. There are a lot of frontier model players that are very competent (OpenAI, Anthropic, Google, Amazon, DeepSeek, xAI) and I'm sure more will come onboard as we find ways to make models smaller and smaller. The mental framework I try to use is that AI is this weird technology that is an enabler of a lot of downstream technology, with the best economic analogy being electricity. It'll change our society in very radical ways, but it's unclear who's going to make money off of it. In the electricity era Westinghouse and GE emerged as the behemoths because of their ability to manufacture massive turbines (which are the equivalent of today's NVIDIA and perhaps Google).
• bhouston 2y ago
  In order to win you often have to start before the problem is fully solved and then count on it being solved better as you build and scale. Thus starting with engineering effort to fit some of the AI limitations make sense but realize many of those will be temporary and will be replaced in the future. But there is always a new tech or framework or something that emerges after you start and adopting it will improve your product measurably.
• bko 2y ago
  It's a little depressing how many high valued startups are basically just wrappers around LLMs that they don't own. I'd be curious to see what percentage of YC latest batch is just this. > 70% of Y Combinator’s Winter 2024 batch are AI startups. This is compared to -57% of YC Summer 2023 companies and ~32% from the Winter batch one year ago (YC W23). The thinking is, the models will get better which will improve our product, but in reality, like the article states, the generalized models get better so your value add diminished as there's no need to fine tune. On the other hand the crypto fund made a killing off of "me too" block chain technology before it got hammered again. So who knows about 2-5 year term but 10 year almost certainly won't have these billion dollar companies that are wrappers around LLMs https://x.com/natashamalpani/status/1772609994610835505?mx=2 https://x.com/natashamalpani/status/1772609994610835505?mx=2
  • scarface_74 2y ago
    How is being a wrapper for LLMs you don’t own any different from being a company based on cloud infrastructure you don’t own? LLMs are a platform. Bill Gates definition of a platform was “A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it.”
    • immibis 2y ago
      A LLM wrapper adds near-zero value. If I type some text into a "convert to Donald Trump style" tool, it produces the exact same output as typing it into ChatGPT following "Convert this text to Donald Trump style:" because that's what the tool actually does. Implementing ChatGPT is 99.999% of the value creation. Prepending the prompt is 0.001%. The surprising fact is that the market assigns a non-zero value to the tool anyway. Startups that use cloud servers still write the software that goes on those servers, which is 90% of the value creation.
      • scarface_74 2y ago
        That’s not what I see from the companies I work with (cloud consulting). Almost all of them are using LLMs along with “tools” and RAG.
• alganet 2y ago
  There is a circular argument going on in this article. Basically: > "Flexible is better. Why? Because specific has been consistently worse" I mean, I don't deny the historical trend. But really, why is it so? It makes sense to follow the trend, but knowing more about the reason why would be cool. Also, I feel that the human cognitive aspects of "engineering some shit" are being ignored. People are engineering solutions not only to be efficient, but to get to specific vantage points in which they can see further. They do it so they can see what the "next gen flexible stuff" looks like before others. Finally, it assumes the option to scale computation is always available and ignores the diminishing returns of trying to scale vanguard technology. The scale requirements for AI stuff are getting silly real fast due to this unshakable belief in infinite scaling. To me, they're already too silly. Maybe we need to cool down, engineer some stuff and figure out where the comfortable treshold lies.
  • HarHarVeryFunny 2y ago
    > But really, why is it so? I don't think the bitter lesson is (or should be?) that you don't need to engineer biases in because with enough compute you could learn them instead ... The real lesson is that finding the right (minimal) set of biases is hard, and you should therefore always learn them instead if possible. Systems that learn are liable to outperform those that don't because by letting the data speak for itself, they are learning the right biases. I think this is why historically the bitter lesson has been correct - because people have built too much into AI systems, got it wrong, and made them limited and brittle as a result. The same thing is continuing to happen today with pre-trained LLMs (a fixed set of patterns!) and bespoke test-time reasoning approaches (of which at most one, likely none, are optimal). Of course it's understandable how we got here, even if a bit like the drunk looking for his lost car keys under the street lamp rather than in the dark spot he lost them. Continuous incremental learning (no training vs inference time distinction) is an unsolved problem, and the basis of intelligence/reasoning is not widely understood.
    • alganet 2y ago
      > finding the right (minimal) set of biases is hard I'm not familiar with this concept of a minimal set of biases. The way I see it, it's a series of loosely community-defined tresholds. If there's something like a theory that defines those biases in a formal way, I would very much like to read it.
• sd9 2y ago
  I think there is value in companies built around AI. The value comes from UX, proprietary supplementary datasets, and market capture. Businesses built now will be best positioned to take advantage of future improvements in general AI. That is why I am building in the AI space. I’m not naive to the predictable improvement in foundational models.
• Havoc 2y ago
  Conceptually that seems right, but think there is a decade+ worth of runway left on working in the "glue" space. i.e. connecting the AI and our lived realities. I don't see the general-ness of AI overcoming the challenges there...cause there is a lot of non-obvious messiness there.
  • danielmarkbruce 2y ago
    It's endless. Hence the endless SaaS, despite it all react UI wrapping a postgres DB or similar.
• scosman 2y ago
  Great essay. This is 100% right about the technical side, but I think it misses the “product” aspect. Building a quality product (AI or otherwise), involves design and data at all levels: UX, on-boarding, marketing, etc. The companies learning important verticals and getting in the door with customers will have a pretty huge advantage as models get better. Both in terms of install base, and knowing what customers need. Really great products don’t simply do what a customer asks, but are built by taking to a ton of customers over and over, and solving their problems better than any one of them can articulate. It’s true we will need less and less custom software for problems. But it isn’t realistic to say the software wrapper effort is going to zero when models improve. Plus: a lot of software effort is needed for getting the data AI needs. This is going to be a huge area - think Google maps with satellites, camera cars, network effect products (ratings), data collection (maps, traffic), etc.
  • nopcode 2y ago
    This all sounds very similar to the hundreds of excel/spreadsheet "killers" out there. But excel math is easy to outperform, ChatGPT etc wont be.
    • scosman 2y ago
      Sure. But it also sounds very similar to every successful database-backed company where each tenant has < 1m rows (almost all of them).
• Devasta 2y ago
  This assumes that the AI bros care about building useful products. They don't for the most part, they care only about building a startup that can plausibly get VC funding. So what if in five years your LLM on the blackchain app fails, you got to blow throw a few million dollars over a few years going around to conferences and living the high life. That's a success in anyone's book.
• ryaniscool 2y ago
  I feel like the author glosses over some nuance in the point he is trying to make and his conclusion doesn't incorporate the nuance. The nuance is that even though AI researchers have learned this lesson, they are still building purpose built AIs because it's not possible to build an AI that can learn to do anything (i.e. AGI). Therefore, building on top of an existing AI model to meet a vertical market demand is not that crazy. It's the same risk when building on any software platform. That is the provider of the platform may add the feature/app you are building as a free addition and your business becomes obsolete.
  • danielmarkbruce 2y ago
    And they are generating oodles of training data which... goes step by step on how to do specific tasks....
• leviliebvin 2y ago
  Controversial opinion: I don't believe in the bitter lesson. I just think that the current DNN+SGD approaches are just not that good at learning deep general expressive patterns. With less inductive bias the model memorizes a lot of scenarios and is able to emulate whatever real work scenario you are trying to make the model learn. However it fails to simulate this scenario well. So it's kind of misleading to say that it's generally better to have less inductive bias. That is only true if your model architecture and optimization approach are just a bit crap. My second controversial point regarding AI research and startups: doing research sucks. It's risky business. You are not guaranteed success. If you make it, your competitors will be hot on your tail and you will have to keep improving all the time. I personally would rather leave the model building to someone else and focus more on building products with the available models. There are exceptions like finetuning for your specific product or training bespoke models for very specific tasks at hand.
  • marcosdumay 2y ago
    > I just think that the current DNN+SGD approaches are just not that good I'll add even further. The transformers and etc that we are using today are not good either. That's evidenced by the enormous amount of memory they need to do any task. We have just taken the one approach that was working a bit better for sensorial tasks and pattern matching, and went all in, adding hardware after hardware so we could brute-force some cognitive tasks out of it. If we do the same to other ML architectures, I don't think they would stay much behind. And maybe some would get even better results.
  • clomond 2y ago
    I also don't believe in the 'bitter lesson' when extrapolated to apply to all 'AI application layer implementations' - at least in the context of asserting that the universe of problem scopes are affected by it. I think it is true in an AI research context, but an unstated assumption is that you have complete data, E2E training, and the particular evaluated solution is not real-world unbounded. It assumes infinite data, and it assumes the ability to falsify the resulting model output. Most valuable, 'real world' applications of AI when trying to implement in practice have an issue with one or both of those. So in other words: where a fully unsupervised AI pathway is viable due to the structure of the problem, absolutely. I'm not convinced in the universality of this. Doesn't mean the core point of this essay on the futility of startups basing their business around one of the off the shelf LLMs isn't valid - I think for many they risk being generalized away.
• motbus3 2y ago
  I agree the article presents good points specially if you consider current LLM models and the business models (and questionable business practices) being used. It seems unlikely at this point, but it it might be the case that new methods, models and/or algorithms rise due to the trend of having high expectations/investments on this area But yes, AI business stuff will be Swallowed by those companies as much as the companies that unwillingly are providing content to those companies.
• iandanforth 2y ago
  I disagree with the author based on timelines and VC behaviour. There is sufficient time to create a product and raise massive capital before the next massive breakthrough hands the value back to OpenAI/Google/Anthropic/MS. Secondly the execution of a solution in a vertical is sufficiently differentiating that even if the underlying problem could be solved by a next gen model there's very little reason to believe it will be. Big Cos don't have the interest to attack niches while there are billion user markets to go after. So don't build "photo share with AI", build "plant fungus photo share for farmers".
  • jvanderbot 2y ago
    Yeah I can't make this article work as a day to day advice piece. In the time it takes to get a generational change in AI from computational resources, we might find a worthy result for your PhD, business, drug design, or killer application. It can be simultaneously true that a tech is doomed to obsolescence from over specialization, and that it does incredibly useful things in the mean time.
• danielovichdk 2y ago
  AI founders will also make sure that users - the ones that are not capable of telling if you're lying to them by taking their money and energy - will learn the bitter lesson of taking it right up the arse. Again. Just like a repetition of history. Humanity is like ever before caught in the action of serving the individuals that made us fuck ourselves over. And the only thing you do is thanking them for it by applying to their tactics. Middle class humanity and above is morally bankrupt, and AI is the next thing they will fight over and with, but of course without adding anything positive to the world.
• jvanderbot 2y ago
  TFA admits that specialization can gain a temporary edge. But, says using that specialization is useless because the next gen will eclipse that edge using raw CPU. Even if it is true that the next generational change in AI is based on computational improvements, how can it be true that it's hopeless to build products by specializing this generation of tech? Moreover if I specialize gen 1, and gen 2 is similar enough, can't specialization maintain an edge? There seems to be a timescale mismatch.
• osigurdson 2y ago
  I does feel like we are having the petfood.com moment in B2B AI. Bespoke solutions, bespoke offerings for very narrow B2B needs. Of course waiting around for AI to get so good that no bespoke solution is needed might be a bad strategy as well. I'm not sure how it will play out but I am certain there will be significant consolidation in the B2B agent space.
  • tim333 2y ago
    In the UK I bought most of my pet food from zooplus.co.uk. I don't think there's anything inherently wrong with the petfood.com idea, it's just that the best executing company will win as usual.
    • osigurdson 2y ago
      Correction, I meant pets.com. Liquidated in 2002 for $125K, while Amazon is worth $2T. My point, generally, is the market doesn't want overly specialized products.
      • gwd 2y ago
        OTOH, Amazon did start by focusing exclusively on mastering the online book market.
        • osigurdson 2y ago
          Yes, but they never ended to only sell books.
• darepublic 2y ago
  Dunno why anyone in the startup space would be excited about new more powerful AI models unless they are just throwing a mask over their existential fears
• EGreg 2y ago
  I was going to keep this to myself to maintain a competitive advantage, but I will just drop two hints: 1) Have AI turn natural language interactions into programs 2) Use test-driven development on the domain data There is a third thing that’s far more crucial but if you want to find out what it is, contact me. I’m building a general-purpose solution for a few domains. Unlike the other two platforms I built (Web 2.0 [1] and Web 3.0 [2]) I am not planning to open-source this project. So I don’t want to discuss more. I will however say that open source is a huge game-changer — because organizations / customers want to be able to be in control and you don’t want to be reliant on some third-party platform for an API provider. We all know how well that one turned out for startups building on Web 2.0 platforms. 1. https://github.com/Qbix https://github.com/Qbix 2. https://github.com/Intercoin https://github.com/Intercoin
  • mdaniel 2y ago
    Isn't the premise of TDD to do that process in the opposite order? Anyway, I don't follow what you're premise is about the relationship between "open source being a game changer," but then "customers want control," and then "startups are not successful on Web 2.0 platforms". Maybe you're trying to cram too much insight into one sentence and I'm not seeing what the thesis is
    • EGreg 2y ago
      Basically, if you rely on some external computers owned by others to do your work (API, external platform, etc) they can pull the rug from under you, or change the rules, pricing etc. They can misuse your data and more. That’s why I built these open source solutions, so our customers can know that it will never change, THEY choose the hosting, THEY own the data, they can even hire a different certified developer or agency to add features or maintain it. They have all the choice, and they have a direct relationship with all their members.
• DesaiAshu 2y ago
  This (particularly the figure 1 illustration) discounts the "distribution" layer for apps Single app/feature startups will lose (true long before AI). A few will grow large enough to entrench distribution and offer a suite of services, creating defensibility against competitors The distributors (eg. a SaaS startup that rapidly landed/expanded) will continue to find bleeding edge ways to offer a 6-12mo advantage against foundation models and incumbents GitLab is a great example of this model. The equivalent bitter lesson of the web is that every cutting edge proprietary technology will eventually be offered free open source. However, there is a commercial advantage to purchasing the bleeding edge features with a strong SLA and customer service The mistake is to think technology is a business. Business has always been about business. Good technology reduces the cost of sale (CAC) and cost of goods sold (COGS) to create a 85-90% margin. Good technology does not create a moat Resilient businesses do not rely on singular technology advantages. They invest heavily in long term R&D to stay ahead of EACH wave. Resting on one's laurels after catching a single wave, or sitting out of the competition because there will be bigger waves later, are both surefire ways to lose the competition
• highfrequency 2y ago
  One day LLMs may replace traditional search engines… in the meantime Google has built a $2 trillion business on specialized engineering and millions of human-designed features and optimizations. The Bitter Lesson is an elegant asymptotic result. But from a business perspective it pays to distinguish problems that general deep learning approaches will disrupt in 1 year vs. 5 years vs. 30 years.
• xchip 2y ago
  thanks for the tl;dr
• asdev 2y ago
  The main point here seems to be around boxing in the AI with a rigid set of rules, and then not being able to adapt to model improvements over time. I think if those "rules" are just in the prompt, you can still adapt, but if you're starting to code a lot of rule based logic in the product, you can get into trouble.
• theptip 2y ago
  I think this misses a crucial dynamic. It’s not “custom scaffolding vs. wait for better models”. There is also fine-tuning. In specialized domains you can’t just rely on OpenAI finding all the training data required to render your experts obsolete. If you can build a data flywheel you can fine-tune models and build that lead into a moat; whoever has the best training set will have the best product. In the short term you might start fine-tuning on OpenAI but once you’ve established your dataset you can potentially gain independence by moving onto OSS models. If you are building a software assistant, sure, this is clearly something that OpenAI will get better at very quickly, and Altman has commented as much that many companies are building things which are certain to get steamrolled by core product development. But I think there is a very interesting strategic question around areas like law, medicine, and probably more so niche technical areas, where OpenAI will/can not absorb knowledge as quickly.
• mlepath 2y ago
  The author appears to be confused about the difference between research and production. In research more generic approaches typically win because they get resourced much better (plus the ever-growing compute and data have helped, but there is no guarantee that these will continue). On production side of "AI" (I don't love the term being thrown around this loosely as true AI should include planning, etc not just inference) the only question is how well do you solve the one problem that's in front of you. In most business usecases today that problem is narrow. LLMs drive a minuscule (but growing) amount of value today. Recommender systems drive huge amount of value. Recommender systems are very specialized.
• AlienRobot 2y ago
  I wish I knew what "AI" is. Please correct me if I'm wrong, but to my understanding, every single one of these "AI" products is based on a model, i.e. it's just a normal program with a very big configuration file. The only thing "AI" about it is the machine learning performed when training the model. At the generation stage, there is nothing "AI" about it anymore. The AI is done already. What runs is a normal program. I know it's used in computer vision, recommendation algorithms, and generative software like ChatGPT, Stable Diffusion, etc. I don't know if there is anything besides these. From what I know, the biggest problems with AI is: 1) the program pretends to be intelligent, when it isn't, e.g. by using natural language. And 2) the program doesn't give the user enough control, they only get one text box prompt and that's it, no forms, no dialogs, so the product has to somehow generate the right answer from the most uncontrolled piece of data imaginable. These two things combined not only limit the products potential by giving it unreasonable expectations but also makes it feel a bit of a scam: the product is a program, but what sells is its presentation: it has to present itself as being MORE than just a program by hiding as much of its internal workings from the consumers and covering its warts with additional layers of logic. I don't know if the author would consider natural language to be a "hardcoding" create to temporarily solve a problem that should be solved by using more compute (AGI), but to me it feels like it is. The best application of AI is probably going to be using AI internally to solve existing problems in a given domain your business is familiar with rather than try to come up with some AI solution that you have to sell other people.
• rnamerl 2y ago
  I think the goal is just to get as much funding from VCs and government rackets as possible. The previous administration had Harris as the "AI czar". Which means that nothing was expected to happen. The following administration has Sacks as the "Crypto & AI czar". I'm not aware that Sacks has any particular competence in the AI area, but he has connections. So government money is likely to flow, presumably to "defense" startups. This All-In podcast has paid off: From peddling gut bacteria supplements and wrong explanations on how moderator rods work in nuclear power plants to major influence.
• crawshaw 2y ago
  The bitter lesson is a wonderful essay and well worth a read: http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html I also believe the original bitter lesson essay is correct. General methods on more powerful compute will supersede anything specific built today. However I believe the lesson in this blog post is an incorrect conclusion. It is roughly analogous to "all people die, so do not bother having children because they will die one day." It is true that what you build today will be replaced in the future. That is OK. Don't get caught out when it happens. But building specific systems today is the only way to make progress with products. Waymo started some 12 years ago as Google Chauffeur, built on all sorts of image/object recognition systems that I am sure have fallen to the bitter lesson. Should Google have refused to start a self-driving car project 12 years ago, because generalized transformers would replace a lot of their work in the future? No. You have to build products with what you've got, where you are. Adopt better tools when they come along.
• tippytippytango 2y ago
  The bitter lesson is a powerful heuristic, but its adherents treat it as dogma and wield it as a club to win arguments.
• anonu 2y ago
  I've observed that every time OpenAI makes an announcement, 2 dozen startups die. The pace was quite rapid a year ago but has slowed down now. I've advised startups of this "bitter lesson". The value is in proprietary data and your ability to build a moat around domain expertise in a specific industry vertical.
• danielmarkbruce 2y ago
  The current, intricate, post training / fine tuning going on in models like 0-1 is feature engineering.
• 2pk03 2y ago
  Right. It boils down to federated learning on edge to have access to relevant data.
• deleted 2y ago
  [deleted]
• anon373839 2y ago
  I think some people have been traumatized by the bitter lesson and now think it’s a fundamental law of nature or something. But application development isn’t just about technology. It isn’t even primarily about technology. It’s about people. Good applications reflect conscious and purposeful design choices based on understanding of the user’s needs. That is where the value-add is. The best application developers deeply understand the interface design space, the quirks and limitations of the underlying technology, their users’ mental models, and how to bring all of this into alignment to deliver a worthwhile product. I see no evidence that LLMs are on the precipice of acquiring (much less mastering) these capabilities.