[HN Gopher] Do AI companies work?
___________________________________________________________________
Do AI companies work?
Author : herbertl
Score : 227 points
Date : 2024-09-29 23:44 UTC (23 hours ago)
(HTM) web link (benn.substack.com)
(TXT) w3m dump (benn.substack.com)
| flappyeagle wrote:
| This 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 wrote:
| Agree completely.
|
| Monetizing all of this is frankly...not my problem.
| leeter wrote:
| I'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 wrote:
| There'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 wrote:
| It 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
| Yizahi wrote:
| Hear me out, I know it is controversial idea, but anyway -
| gaming. :)
| mandevil wrote:
| One 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 wrote:
| No, 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 wrote:
| > _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 wrote:
| Yep, 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 wrote:
| What goodies can I get from AI companies though?
| candiddevmike wrote:
| Your own GPL unencumbered regurgitations of popular GPL
| libraries and applications.
| lxgr wrote:
| Except that copyright law doesn't work that way.
| mrbungie wrote:
| At 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 wrote:
| > _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-
| physi...
| gimmefreestuff wrote:
| VC is the worst possible way to fund entrepeneurs.
|
| Except for all the others.
| JumpCrisscross wrote:
| VC 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 wrote:
| That'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 wrote:
| Where 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 wrote:
| Add 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 wrote:
| For 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.
| rty32 wrote:
| "walk out to the taxi rank"
|
| I assume you are talking about airports. Guess what, they
| still exist in many places. And on the other hand, for US,
| other than a few big cities, the "normal" taxi experience is
| that you call a number and _maybe_ a taxi shows up in half an
| hour. With Uber, that becomes 10 minutes or less, with live
| map updates. Give me that and I 'll be happy to forget about
| Uber.
| baq wrote:
| First VC to exit a nuclear reactor profitably is going to be
| something.
| xenospn wrote:
| Unless we're trying to get an actual company funded without
| giving away our worthless product for free.
| est wrote:
| AI as a work force could be comparable to interns. They work in a
| 7x24 shift but fail the task from time to time.
| themanmaran wrote:
| Companies 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 wrote:
| the 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 wrote:
| can you share some of these heuristics you referred to?
| AI_beffr wrote:
| the turing test
| jfengel wrote:
| I 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 wrote:
| Did you criticize the turing test as being meaningless before
| it was easily passed by LLMs? if not i don't see how you can
| avoid updating on "this is getting more human" or at least
| "this is getting closer to intelligence" to avoid the human-
| bias
| jfengel wrote:
| I never gave much thought to the Turing test one way or the
| other. It never struck me as especially informative.
|
| I've always been more interested in the non-verbal aspects
| of human intelligence. I believe that "true AGI", whatever
| that is, is likely to be able to mimic sentient but non-
| verbal species. I'd like to see an AGI do what a dog or cat
| does.
|
| LLMs are quite astonishing at mimicking something humans
| specifically do, the most "rational" parts of our brain.
| But they seem to jump past the basic, non-rational parts of
| the brain. And I don't think we'll see it as "true AGI"
| until it does that -- whatever that is.
|
| I'm reminded of the early AI researchers who taught AI to
| play chess because it's what smart people do, but it
| couldn't do any of the things dumb people do. I think the
| biggest question right now is whether our present
| techniques are a misleading local maximum, or if we're on
| the right slope and just need to keep climbing.
| snapcaster wrote:
| What do you think a dog or cat does that can't be
| replicated by LLMs?
| jfengel wrote:
| I don't know. I know that any particular thing I say
| could be met with an LLM that did that thing. But at the
| moment an LLM doesn't seem to be capable of coding itself
| up that way. Yet.
| snapcaster wrote:
| I guess that was kind of my question when I thought about
| what you said. I could think of any things that a cat or
| dog do that _current_ LLMs can't do but nothing that
| seems fundamentally out of reach
| skydhash wrote:
| Act on its own?
| snapcaster wrote:
| What does it mean for something to "act on its own"?
| Every action taken is in some part due to what's
| happening in the environment right? I would argue that
| nothing "acts on it's own"
| JumpCrisscross wrote:
| AGI is PR. The money is in automating low-level customer
| service, coding and data science.
| empath75 wrote:
| That's just the low hanging fruit. People are going to be
| moving further up the "intelligence" stack as systems
| integrating AI get more sophisticated.
| mvdtnz wrote:
| I can't believe I'm reading this kind of stuff about crappy
| chat bots. The world has lost its mind.
| bcherny wrote:
| This 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 wrote:
| To 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 wrote:
| You may be forgetting that ChatGPT has 10M paying customers.
| Not to mention everyone that pays for Claude Pro, Perplexity
| Pro, and so on.
| cratermoon wrote:
| > OpenAI has 10M paying customers
|
| According to who?
| bcherny wrote:
| > OpenAI COO Says ChatGPT Passed 11 Million Paying
| Subscribers
|
| https://www.theinformation.com/articles/openai-coo-says-
| chat...
| wslh wrote:
| Not 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/op
| enai-hi...
| horsawlarway wrote:
| The math on this doesn't work.
| JumpCrisscross wrote:
| ...go on?
| actsasbuffoon wrote:
| I'm assuming they mean that if you multiply subscribers
| by the subscription fee then OpenAI still ends up losing
| billions per year.
| tkgally wrote:
| One somewhat obsessive customer here: I pay for and use Claude,
| ChatGPT, Gemini, Perplexity, and one or two others.
|
| The UX differences among the models are indeed becoming clearer
| and more important. Claude's Artifacts and Projects are really
| handy as is ChatGPT's Advanced Voice mode. Perplexity is great
| when I need a summary of recent events. Google isn't charging
| for it yet, but NotebookLM is very useful in its own way as
| well.
|
| When I test the underlying models directly, it's hard for me to
| be sure which is better for my purposes. But those add-on
| features make a clear differentiation between the providers,
| and I can easily see consumers choosing one or another based on
| them.
|
| I haven't been following recent developments in the companies'
| APIs, but I imagine that they are trying to differentiate
| themselves there as well.
| streetcat1 wrote:
| The 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_ wrote:
| What 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 wrote:
| Like 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 wrote:
| > 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_ wrote:
| > 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 wrote:
| I would say, without qualification, "no".
| https://www.techpolicy.press/challenging-the-myths-of-genera...
| Der_Einzige wrote:
| #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 wrote:
| I'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=perplex...
| AlienRobot wrote:
| That's a very interesting phenomenon!
| riku_iki wrote:
| > 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.
| llm_trw wrote:
| The 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.
| analog31 wrote:
| On an amusing note, I've read something similar: Everything
| that works stops being called philosophy. Science and math
| being the two familiar examples.
| nine_k wrote:
| I won't say that things like stoicism or humanism never
| worked. But they never got to the level of _strict_ logical
| or experimental verifiability. Physics may be hard science,
| but the very _notion_ of hard science, hypotheses, demand to
| replicate, demand to be able to falsify, etc, are all
| philosophy.
| benterix wrote:
| There is a reason for that. People who inquired into the
| actual functioning of the world used to be called
| philosophers. That's why so many foundations of mathematics
| actually come from philosophers. The split happened around
| the 17th century. Newton still called his monumental work
| "Natural Philosophy", not "Physics".
| benrutter wrote:
| Just in case anyone's curious, this is from Bertrand
| Russell's "the history of philosophy".
|
| > As soon as definite knowledge concerning any subject
| becomes possible, this subject ceases to be called
| philosophy, and becomes a separate science.
|
| I'm not actually sure I agree with it, especially in light of
| less provable schools of science like string theory or some
| branches of economics, but it's a great idea.
| xenospn wrote:
| Is this why almost all CompSci PhDs are actually "doctor of
| philosophy"?
| lostphilosopher wrote:
| PhD itself is an abbreviation for "Doctor of Philosophy."
| The title is more about the original Greek "lover of
| wisdom" than about the modern academic discipline of
| philosophy.
| https://en.wikipedia.org/wiki/Doctor_of_Philosophy
|
| Doctor is similar - in the US, when someone says "Doctor"
| they usually mean "Medical Doctor" but "Doctor" just
| comes from the Greek "teacher" / "scholar" which is more
| broad and the title can still be used officially and
| correctly for PhDs.
| https://en.wikipedia.org/wiki/Doctor_(title)
| sapphicsnail wrote:
| Just a little correction. Doctor is Latin and roughly
| means "someone who has learned a lot."
|
| Science also originally referred to knowledge. What we
| think of as "science" used to be called the natural
| sciences. Sometimes people get confused because I have a
| B.S. in Classics because science has lost that broader
| meaning.
| tovej wrote:
| It is still called natural science, but it used to be
| called natural philosophy.
|
| And it is interesting, as you say, that when it comes to
| Bachelor/Master/Doctor of Science/Art/Philosophy (even
| professor), these are all titles formed from arbitrary
| terms that have been enshrined by the institutions that
| give people these titles.
| analog31 wrote:
| Indeed, in the _Summa Theologica_ , Thomas Aquinas asks
| if theology is a science, and concludes that it is. He
| also gave lip service to logical rigor and
| falsifiability, in the latter case by encouraging the
| discipline of asking contrary questions and answering
| them. What he didn't do was appeal to empirical data, to
| any great extent.
|
| I think the reasoning behind "doctor of philosophy" may
| be lost to history. All knowing Wikipedia suggests that
| it didn't happen at once. My take was that the
| requirements for a modern PhD were added long after the
| title was adopted.
|
| https://en.wikipedia.org/wiki/Doctor_of_Philosophy
|
| I suspect there was a time when a person could be well
| versed in multiple of what are now separated fields, and
| that you had to be a philosopher to make sense of science
| and math. Also, as science was flexing its own wings,
| claiming to be a philosopher might have been a way to
| gain an air of respectability, just like calling a
| physician "doctor" when the main impact of medicine was
| to kill rich people.
|
| Disclosure: PhD, but ambivalent about titles.
| WhyOhWhyQ wrote:
| In college my Professor told me he was disappointed that
| the string theory hype was dying down because it made for a
| great subfield of algebraic geometry.
| OJFord wrote:
| We've kind of done it in reverse with AI though - 'chatbots'
| have been universally shit for ages, and now we have good ones,
| but they're 'AI'.
| digging wrote:
| Chatbot is the form factor; LLMs are what is being called AI.
| SmarterChild wasn't an LLM.
| eth0up wrote:
| I think when things get really good, which I've no doubt will
| happen, we'll call it SI; Synthetic Intelligence
|
| But it may be a while.
| llm_trw wrote:
| Bert is already not an LLM and the vector embedding it
| generates are not AI. It is also first general solution for
| natural language search anyone has come up with. We call them
| vector databases. Again I'd wager this is because they
| actually work.
| surprisetalk wrote:
| Totally agree!
|
| [1] https://taylor.town/synthetic-intelligence
| eth0up wrote:
| That pretty much expounds and articulates my thoughts on
| that subject.
|
| Just don't use the word Champaign anywhere near a Miller
| Highlife in France. I think they're digging up old
| guillotines for that.
|
| Edit: Your Dijkstra quote may be missing an "is":
|
| ..the question whether machines can think _is_ as relevant
| iterateoften wrote:
| I remember the original Lisp manual describes it as a symbolic
| language for "AI"
| hollerith wrote:
| Lisp's inventor, John McCarthy, was an AI researcher. (The US
| government started funding AI research in the 1950s,
| expecting progress to be much faster than it actually was.)
| RHSeeger wrote:
| When I studied AI in grad school (decades ago), most of the
| classes were done in Lisp.
| akomtu wrote:
| People innately believe that intelligence isn't an algorithm.
| When a complex problem presents itself for the first time,
| people think "oh, this must be so complex that no algorithm can
| solve it, only AI," and when an algorithmic solution is found,
| people realise that the problem isn't that complex.
|
| Indeed, if AI was an algorithm, imagine what would it feel like
| to be like one: at every step of your thinking process you are
| dragged by the iron hand of the algorithm, you have no agency
| in decision making, for every step is pre-determined already,
| and you're left the role of an observer. The algorithm leaves
| no room for intelligence.
| pas wrote:
| it mostly depends on one's definition of an algorithm.
|
| our brain is mostly scatter-gather with fuzzy pattern
| matching that loops back on itself. which is a nice loop,
| inputs feeding in, found patterns producing outputs and then
| it echoes back for some learning.
|
| but of course most of it is noise, filtered out, most of the
| output is also just routine, most of the learning happens
| early when there's a big difference between the "echo" and
| the following inputs.
|
| it's a huge self-referential state-machine. of course running
| it feels normal, because we have an internal model of
| ourselves, we ran it too, and if things are going as usual,
| it's giving the usual output. (and when the "baseline" is out
| of whack then even we have the psychopathologies.)
|
| https://www.youtube.com/watch?v=-rxXoiQyQVc
| mdavidn wrote:
| Is that not the human experience? I have no "agency" over the
| next thought to pop into my head. I "feel" like I can choose
| where to focus attention, but that too is a predictable
| outcome arising from the integration of my embryology,
| memories, and recently reinforced behaviors. "I" am merely an
| observer of my own mental state.
|
| But that is an uncomfortable idea for most people.
| gbalduzzi wrote:
| While technically true, the difference is that we don't
| know in details how that process works and we can't predict
| it.
|
| There is not much of a difference in considering it
| deterministic or not, because we can't determine it.
| akomtu wrote:
| If this was true, you could lay back relaxed and watch
| where your brain takes you. But we experience life as a
| never ending stream of choices, usually between what's easy
| and what's right, and pursuing the right choice takes a
| constant effort. We are presented with problems and have to
| figure out solutions on our own, with no guarantees of
| success.
|
| This "I'm just an observer" idea may be true at some higher
| level, if you're a monk on the threshold of nirvana, but
| for common folk this mindset leads nowhere.
| digging wrote:
| > pursuing the right choice
|
| I don't agree with this framing. A person's opinion of
| what a "good" or "right" outcome is _is one of the inputs
| to the algorithm_.
|
| When you decide to put in less effort it's affecting the
| algorithm because the weight of the high-effort outcome
| is reduced.
| tempfile wrote:
| The other option you don't mention is "algorithms can solve
| it, but they do something different to what humans do".
| That's what happened with Go and Chess, for example.
|
| I agree with you that people don't consider intelligence as
| fundamentally algorithmic. But I think the appeal of
| algorithmic intelligence comes from the fact that a lot of
| intelligent behaviours (strategic thinking, decomposing a
| problem into subproblems, planning) _are_ (or at least feel)
| algorithmic.
| fsndz wrote:
| Exactly what I wrote recently: "The "AI effect" is behind some
| of the current confusion. As John McCarthy, AI pioneer who
| coined the term "artificial intelligence," once said: "As soon
| as it works, no one calls it AI anymore." This is why we often
| hear that AI is "far from existing." This led to the
| formulation of the Tesler's Theorem: "AI is whatever hasn't
| been done yet."" https://www.lycee.ai/blog/there-are-indeed-
| artificial-intell...
| beryilma wrote:
| > As soon as it works, no one calls it AI anymore.
|
| So, what are good examples of some things that we used to
| call AI, which we don't call AI anymore because they work?
| All the examples that come to my mind (recommendation
| engines, etc.) do not have any real societal benefits.
| delecti wrote:
| Some examples are navigation algorithms, machine learning,
| neural networks, fuzzy logic, or computer vision. I
| personally learned several of those in a "Artificial
| Intelligence" CS course ~15 years ago, but most people
| would never think to call Google Maps, a smart thermostat
| learning their habits, or their doorbell camera recognizing
| faces "AI".
|
| It's only recently with generative AI that you see any
| examples of the opposite, people outside the field calling
| LLMs or image generation "AI".
| JohnFen wrote:
| I was deeply involved in voice recognition and OCR back in the
| day, and nobody working on these things called them "AI".
|
| I don't doubt that there were marketing people wanting to
| attach an AI label to them, but that's just marketing BS.
| ninetyninenine wrote:
| Yeah but honestly we all know LLMs are different then say some
| chess ai.
|
| You can thank social media for dumbing down a human
| technological milestone in artificial intelligence. I bet if
| there was social media around when we landed on the moon you'd
| get a lot of self important people rolling their eyes at the
| whole thing too.
| cgearhart wrote:
| This is a pretty common perspective that was introduced to me
| as "shifting the goalposts" in school. I have always found it a
| disingenuous argument because it's applied so narrowly.
|
| Humans are intelligent + humans play go => playing go is
| intelligent
|
| Humans are intelligent + humans do algebra => doing algebra is
| intelligent
|
| Meanwhile, humans in general are pretty terrible at exact,
| instantaneous arithmetic. But we aren't claiming that computers
| are intelligent because they're great at it.
|
| Building a machine that does a narrowly defined task better
| than a human is an achievement, but it's not intelligence.
|
| Although, in the case of LLMs, in context learning is the
| closest thing I've seen to breaking free from the single-
| purpose nature of traditional ML/AI systems. It's been
| interesting to watch for the past couple years because I still
| don't think they're "intelligent", but it's not just because
| they're one trick ponies anymore. (So maybe the goalposts
| really are shifting?) I can't quite articulate yet what I think
| is missing from current AI to bridge the gap.
| Scarblac wrote:
| > Meanwhile, humans in general are pretty terrible at exact,
| instantaneous arithmetic. But we aren't claiming that
| computers are intelligent because they're great at it.
|
| "The question of whether a computer can think is no more
| interesting than the question of whether a submarine can
| swim." - Edsger Dijkstra
| namaria wrote:
| I've said it before, AI is the default way to hype technology.
|
| A hundred years ago computers were 'electronic brains'.
|
| It goes dormant after loosing its edge and then re-emerges when
| some research project gains traction again.
| anodari wrote:
| In the same line, there are also a phrase about technology, "is
| everything that doesn't work yet." by Danny Hillis, "Electric
| motors were once technology - they were new and did not work
| well. As they evolved, they seem to disappear, even though they
| proliferated and were embedded by the scores into our homes and
| offices. They work perfectly, silently, unminded, so they no
| longer register as "technology."
| https://kk.org/thetechnium/everything-that/
| plaidfuji wrote:
| I 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 wrote:
| > 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 wrote:
| Having 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 wrote:
| It 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 wrote:
| Humans 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 wrote:
| How is this relevant to AI? An AI can be trained on the total
| knowledge of every field.
| nine_k wrote:
| It'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.
| fsndz wrote:
| LLMs are basically becoming commodities:
| https://www.lycee.ai/blog/why-large-language-models-are-a-co...
| patrickhogan1 wrote:
| ChatGPT 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 wrote:
| It 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 $$$$ and
| that cannot be easily replicated by competition.
|
| Nobody is sitting in the seats at Macworld stomping their feet
| for Sam Altman. A big part of that is giving customers more than
| specs or fiddly features.
|
| These companies need to start building a brand _fast_.
| SamBam wrote:
| > If the proprietary models stop moving forward, the open source
| ones will quickly close the gap.
|
| This is the Red Queen hypothesis in evolution. You have to keep
| running faster just to stay in place.
|
| On it's face, this _does_ seem like a sound argument that all the
| $$ 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 wrote:
| > _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 wrote:
| Why 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.
| JumpCrisscross wrote:
| > _In six months, somebody else is at the asymptote as
| well_
|
| But you were there first. That gives you all of a first
| mover's advantages plus the profits from that period to set
| the terms of the market sharing.
|
| None of this requires winner-takes-all economics. And given
| OpenAI's revenues, it's unclear they need to be the sole
| winner to make an investment thesis work.
| sqeaky wrote:
| The first mover advantage isn't as big an advantage as
| being the one to monopolize a commodity. While there are
| some historical advantages to first mover's doing this,
| it is the common case. I also think that is very nearly a
| winner take all sort of thing, or at least a very API
| providers will still matters, if this asymptotes.
|
| Compare this to toilet paper or fuel, if AI asymptotes
| and everyone catches up in 6 months it will be be just
| like these. There are some minor differences in quality,
| but if a customer can buy something good enough to keep
| their ass clean or their car powered then they will tend
| to pick the cheapest option.
|
| Sometimes it is subterfuge like ms stealing the OS market
| from IBM or there might be a state sponsored AI lab like
| so many state run industries pushing out competition, but
| there are a ton of ways to beat first movers.
| JumpCrisscross wrote:
| If there is no niche, first-mover is useless. But if
| there is value to be had, it's valuable. Fighting to
| tread water in a moving stream with the expectation the
| flow will slow isn't irrational.
| sqeaky wrote:
| Are you claiming there is no Niche to making commodities?
| I haven't really considered it I could see an argument
| being made either way. Or are you saying that it's a
| product these API based llms are just not going to be how
| we use these tools? Or are you saying something else
| entirely?
| jsnell wrote:
| Ok, so let's say I was there first. But now you're
| offering a product that's (by definition) just as good
| for a lower price. Why will the users stick to my product
| rather than paying your cheaper prices?
|
| As for your second point, the profits from the first six
| months are going to be basically nothing compared to the
| years of massive investments needed to make this happen.
| Why would those profits give any kind of sustainable
| leverage?
| actsasbuffoon wrote:
| Excellent point, but now consider the availability of open
| source models as well. They're not as good as the frontier
| models, but at some point they get close enough, right?
|
| I run Llama 3.x locally on my 5+ year old GPU. It's pretty
| decent. It's not as smart as GPT-4 or Claude, but it's good
| enough for a lot of what I do, and most importantly there's
| no cap on the number of prompts I can send in a given
| timeframe.
|
| For many consumers, it's fine to be a little bit behind the
| cutting edge. My smartphone is 2 models behind now. It's
| fast enough, the camera is good enough, and the battery is
| holding up just fine. I don't feel any need to fork over
| $1,000 for a new one right now.
|
| I think the same phenomenon will become more common for AI
| enthusiasts. It's hard to beat free.
|
| And what happens to the economics of building frontier
| models then? I suspect a lot of air is going to be released
| from the AI bubble in the next couple of years.
| gdiamos wrote:
| The 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 wrote:
| >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 wrote:
| No, 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 wrote:
| Yes, the "surplus compute power" story doesn't hold water.
| The magic of AWS is in its software and control plane.
| agentultra wrote:
| They 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 wrote:
| Don'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 wrote:
| Enjoyed 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.
| bbqfog wrote:
| The infrastructure hardware and software is a commodity. Any
| real moat comes from access to data. I think we've seen data
| close up quite a bit since people realized that you can train
| LLMs with it, so I don't know that OpenAI's data access is
| better than when they trained GPT 4. In fact, it's probably
| worse unless they've cut independent deals with massive data
| providers.
| empath75 wrote:
| Inference costs many many times less than training. I think we
| may end up with one more round of training a larger
| foundational model, and then it's going to have to wait for
| new, cheaper, more efficient hardware. It's quite possible that
| all of these first mover companies go bankrupt, but the models
| will still exist generating value.
| bhouston wrote:
| I 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 wrote:
| Likely 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 wrote:
| And brains do it on about 20 watts.
| asterix_pano wrote:
| Training takes many years though.
| namaria wrote:
| Training took hundreds of thousands of years. Everyone
| just gets a foundation model to fine tune for another
| couple of decades before it can start flipping burgers.
| thfuran wrote:
| It's been something like half a billion years since the
| first brain.
| namaria wrote:
| That was a much smaller model, couldn't do much more than
| crawl around and run away.
| thfuran wrote:
| It was still used as part of pretraining the current
| model.
| namaria wrote:
| Nonsense, the current model is a new architectural
| approach.
|
| It was all explained in that recent paper, "Attention is
| all your meat"
| foobiekr wrote:
| In particular:
|
| "There is, however, one enormous difference that I didn't think
| about: You can't build a cloud vendor overnight. Azure doesn't
| have to worry about a few executives leaving and building a
| worldwide network of data centers in 18 months."
|
| This isn't true at all. There are like 8 of these companies
| stood up in the last three or four years fueled by massive
| investment of sovereign funds - mostly the saudi, dubai,
| northern europe, etc. oil-derived funds - all spending billions
| of dollars doing exactly that and getting something done.
|
| The real problem is the ROI on AI spending is.. pretty much
| zero. The commonly asserted use cases are the following:
|
| Chatbots Developer tools RAG/search
|
| Not a one of these is going to generate $10 of additional
| revenue per sollar spent, nor likely even $2. Optimizing your
| customer services representatives from 8 conversations at once
| to an average of 12 or 16 is going to save you a whopping $2
| per hour per CSR. It just isn't huge money. And RAG has many,
| many issues with document permissions that make the current
| approaches bad for enterprises - where the money is - who as a
| group haven't spent much of anything to even make basic search
| work.
| sushid wrote:
| > And RAG has many, many issues with document permissions
|
| Why can't these providers access all documents and when
| answers are prompted, self-censor if the reply has references
| to documents that the end users do not have access
| permissions? In fact, I'm pretty sure that's how existing
| RAGaaS providers are handling document/file permissions.
| portaouflop wrote:
| RAGaas, didn't know I needed that in my life
| bhouston wrote:
| > The real problem is the ROI on AI spending is.. pretty much
| zero.
|
| Companies in user acquisition/growth mode tend to have low
| internal ROI, but remember both Facebook and Google has the
| same issue -- then they introduced ads and all was well with
| their finances. Similar things will happen here.
| HillRat wrote:
| LLMs are a utility, not a platform, and utility markets
| exert a downward pressure on pricing. Moreover, it's not
| obvious that -- once trained models hit the wild -- any one
| actor has or can develop significant competitive moats that
| would allow them to escape that price pressure. Beyond
| that, the digital marginal cost of services needs to be
| significantly reduced to keep these companies in business,
| but more efficient models leads to pushing inference out to
| end-user compute, which hollows out their business model (I
| assume that Apple dropping out of the OpenAI investment
| round was partially due to the wildly optimistic valuations
| involved, partially because they're betting on being able
| to optimize runtime costs down to iPhone levels).
|
| Basically, I'd argue that LLMs look less like a Web 2.0
| social media opportunity and more like Hashicorp or Docker,
| except with operational expenses running many orders of
| magnitude higher with costs scaling linearly to revenue.
| Ekaros wrote:
| And on other side, how many more paying users will there
| be? And of users that known about AI or have tried it are
| happy to use whatever is free at the moment. Or just
| whatever is on Google or Bing with add next to it?
|
| Social media is free, subscription services are for stuff
| you can't get for free easily. But will there actually be
| similar need for AI? Be it any generation.
| bhouston wrote:
| > LLMs are a utility, not a platform, and utility markets
| exert a downward pressure on pricing.
|
| I think competition exerts a downward pressure on
| pricing, not being a utility personally. But I guess I
| agree with the utility analogy in that there are
| massively initial upfront costs and then the marginal
| costs are low.
|
| > more efficient models leads to pushing inference out to
| end-user compute, which hollows out their business model
|
| Faster CPUs have been coming forever but we keep coming
| up with ways of keeping them busy. I suspect the same
| pattern with AI. Thus server-based AI will always be
| better than local. In the future, I expect to be served
| by many dozens of persistent agents acting on my behalf
| (more agents as you go further into the future) and they
| won't be hosted on my smartphone.
| LtWorf wrote:
| I'm running on a decade old computer and it is just fine.
| 10 years ago a decade old computer vs a current one would
| have made a huge difference.
| nineteen999 wrote:
| So I go to ask an LLM to answer to a question and it starts
| trying to sell me products I don't want or need? I'll need
| to filter it's output through a locally run adblock LLM
| which detects/flags/strips out advertising text before
| delivering the output. Hmmm.
| elialbert wrote:
| i think you hit the nail on the head
| FactKnower69 wrote:
| it won't be as easy to enshittify a product no one actually
| wants to use in the first place
| pzs wrote:
| "The real problem is the ROI on AI spending is.. pretty much
| zero. The commonly asserted use cases are the following:
| Chatbots Developer tools RAG/search"
|
| I agree with you that ROI on _most_ AI spending is indeed
| poor, but AI is more than LLM's. Alas, what used to be called
| AI before the onset of the LLM era is not deemed sexy today,
| even though it can still make very good ROI when it is the
| appropriate tool for solving a problem.
| aketchum wrote:
| AI is a term that changes year to year. I don't remember
| where I heard it but I like that definition that "as soon
| as computers can do it well it stops becoming AI and just
| becomes standard tech". Neural Networks were "AI" for a
| while - but if I use a NN for risk underwriting nobody will
| call that AI now. It is "just ML" and not exciting. Will AI
| = LLM forever now? If so what is the next round of
| advancements called?
| Mistletoe wrote:
| https://en.wikipedia.org/wiki/AI_effect
| chatmasta wrote:
| While it might be possible for a deep-pocketed organization
| to spin up a cloud provider overnight, it doesn't mean that
| people will use it. In general, the switching cost of
| migrating compute infrastructure from one service to another
| is much higher than the switching cost of changing the LLM
| used for inference.
|
| Amazon doesn't need to worry about suddenly losing its entire
| customer base to Alibaba, Yandex, or Oracle.
| alexashka wrote:
| > I think we are in the middle of a steep S-curve of technology
| innovation
|
| We are? What innovation?
|
| What do we need innovation _for_? What present societal
| problems can tech innovation possibly address? Surely none of
| the big ones, right? So then is it fit to call technological
| _change_ - 'innovation'?
|
| I'd agree that LLMs improve upon having to read Wikipedia for
| topics I'm interested in but would investing _billions_ in
| Wikipedia and organizing human knowledge have produced a better
| outcome than relying on a magic LLM? Almost certainly, in my
| mind.
|
| You see, people are pouring billions into LLMs and not
| Wikipedia not because it is a better product - but because they
| foresee a possibility of an abusive monopoly and that really
| excites them.
|
| That's not innovation - that's more of the same anti-social
| behaviour that makes any _meaningful_ innovation extremely
| difficult.
| SoftTalker wrote:
| One of Google's founding goals was "organizing human
| knowledge" IIRC. They ended up being an ad company.
| mellosouls wrote:
| I'm not sure the Wikipedia example is a strong one as that
| site has it's own serious problems with "abusive monopolies"
| in its moderator cliques and biases (as with any social
| platform).
|
| At least with the current big AI players there is the
| _potential_ for differentiation through competition.
|
| Unless there is some similar initiative with the Wikipedias,
| the problem of single supplier dominance is a difficult one
| to see as the way forward.
| alexashka wrote:
| I can solve Wikipedia's woes quite easily - Wikipedia
| should limit itself to math, science, engineering,
| medicine, physics, chemistry, geography and other
| disciplines that are not at all in dispute.
|
| Politics, history, religion and other topics of
| conversation that are matters of opinion, taste and state
| sponsored propaganda need to be off limits.
|
| Its mission ought to be to provide a PhD level education in
| all technical fields, not engage in shortening historical
| events and/or opinions/preferences/beliefs down to a few
| pages and disputing which pages need to be left in or out.
| Let fools engage in that task on their own time.
| christianqchung wrote:
| Is this satire? All of those industries listed
| significant disputes. If this is serious it's a
| concerning view of how science actually works.
| alexashka wrote:
| Please name me these 'significant' disputes. Start with
| math.
| kibwen wrote:
| Let's start with an easy one:
| https://en.wikipedia.org/wiki/Axiom_of_choice
| Der_Einzige wrote:
| You forgot to link the opposite (axion of determinancy)
| sapphicsnail wrote:
| A lot of things that seem like simple facts are actually
| abstractions based on abstractions based on abstractions.
| The further up the chain, the more theoretical it gets.
| This is true of the humanities and science.
| Pamar wrote:
| Geography I suppose includes borders. So you have now
| history and politics back in.
| datavirtue wrote:
| Nonetheless, Microsoft is firing up a nuclear reactor to
| power a new data center. My money is in the energy sector
| right now. Obvious boom coming with solar, nuclear and AI.
| ActionHank wrote:
| The car wasn't a horse that was better, but a car has not
| changed drastically since they went mainstream.
|
| They've gotten better, more efficient, loaded with tech, but
| are still roughly 4 seats, 4 doors, 4 wheels, driven by
| petroleum.
|
| I know that this is a massive oversimplification, but I think
| we have seen the "shape" of LLMs\Gen AI\AI products already and
| it's all incremental improvements from here on out with more
| specialization.
|
| We are going to have SUVs, sports cars, and single seater cars,
| not flying cars. AI will be made more fit for purpose for more
| people to use, but isn't going to replace people outright in
| their jobs.
| pavlov wrote:
| Feels like someone might have said this in 1981 about
| personal computers.
|
| "We've pretty much seen their shape. The IBM PC isn't
| fundamentally very different from the Apple II. Probably it's
| just all incremental improvements from here on out."
| ActionHank wrote:
| I would agree with your counter if it weren't for the
| realities of power usage, hardware constraints, evident
| diminishing returns on training larger models, and as
| always the fact that AI is still looking for the problem it
| solves, aside from mass employment.
|
| Computers solved a tangible problem in every area of life,
| AI is being forced everywhere and is arguably failing to
| make a big gain in areas that it should excel.
| adamc wrote:
| What do you think hasn't been?
|
| I think the big game changer in the PC space was graphics
| cards, but since their introduction, it _has_ all been
| incremental improvement -- at first, pretty fast, then...
| slower. Much like CPU improvements, although those started
| earlier.
|
| I can't think of a point where the next generation of PCs
| was astoundingly different from the prior one... just
| better. It used to be that they were reliably faster or
| more capable almost every year, now the rate of
| improvements is almost negligible. (Yes, graphics are
| getting better, but not very fast if you aren't near the
| high end.)
| felideon wrote:
| Arguably form factor, i.e. smartphones and tablets.
| Whether you think that's incremental or not is
| subjective, I guess.
|
| I would say incremental, but it does solve a different
| set of problems (connectedness, mobility) than the PC.
| adamc wrote:
| I would say smartphones and tablets are new devices, not,
| per se, PCs.
|
| Smartphones did bring something huge to the table, and
| exploded accordingly. But here we are, less than 2
| decades from their introduction and... the pace of
| improvement has gone back to being incremental at best.
| kibwen wrote:
| Smartphones and tablets aren't PC replacements, they're
| TV replacements. Even we, deep into the throes of the
| smartphone age, still need to use real computers to do
| the tasks we were using them for in 1995, e.g.
| programming and word processing.
| idunnoman1222 wrote:
| That is basically true about computers minus the networking
| which the Internet brought, but that's a separate beast
| crote wrote:
| > The IBM PC isn't fundamentally very different from the
| Apple II. Probably it's just all incremental improvements
| from here on out.
|
| Honestly? I'm not sure I even disagree with that!
|
| Amazon is just an incremental improvement over mail order
| catalogs. Netflix is just an incremental improvement over
| Blockbusters. UberEats is just an incremental improvement
| over calling a pizzeria. Google Sheets is just an
| incremental improvement over Lotus 1-2-3.
|
| Most of the stuff we're doing these days _could_ have been
| done with 1980s technology - if a bit less polished. Even
| the Cloud is just renting a time slice on a centralized
| mainframe.
|
| With the IBM PC it was already reasonably clear what the
| computer was going to be. Most of the innovation since then
| is "X, but on a computer", "Y, but over the internet", or
| just plain market saturation. I can only think of two truly
| world-changing innovations: 1) smartphones, and 2) social
| media.
|
| The current AI wave is definitely producing interesting
| results, but there is still a _massive_ gap between what it
| can do and what we have been promised. Considering current
| models have essentially been trained on the _entire
| internet_ and they have now poisoned the well and made mass
| gathering of more training data impossible, I doubt we 're
| going to see another two-orders-of-magnitude improvement
| any time soon. If anything, for a lot of applications it's
| probably going to get worse as the training set becomes out
| of date.
|
| And if people aren't willing to pay for the current models,
| they aren't going to pay for a model which hallucinates 50%
| less often. They're going to _need_ that two-orders-of-
| magnitude improvement to actually become world-changing.
| Taking into account how much money those companies are
| losing, are they going to survive the 5-10 years or more
| until they reach that point?
| Ekaros wrote:
| And even smartphone is just combination of with computer,
| but smaller and over internet. It just got pretty good
| eventually. https://en.wikipedia.org/wiki/HP_200LX
| arguably is "smartphone" supporting modem or network
| connectivity, albeit not wireless...
| bangaroo wrote:
| the difference is a personal computer is a vaguely-shaped
| thing that has a few extremely broad properties and there
| is tremendous room inside that definition for things to
| change shape, grow, and improve. processor architecture,
| board design, even the methods of powering the machine can
| change and you still meet the basic definition of "a
| computer."
|
| for better or worse, people saying AI in any capacity right
| now are referring to current-generation generative AI, and
| more specifically diffusion image generation and LLMs.
| that's a very specific category of narrowly-defined
| technologies that don't have a lot of room to grow based on
| the way that they function, and research seems to be
| bearing out that we're starting to reach peak functionality
| and are now just pushing for efficiency. for them to
| improve dramatically or suddenly and radically change would
| require so many innovations or discoveries that they are
| unrecognizable.
|
| what you're doing is more akin to looking at a horse and
| going "i forsee this will last forever because maybe
| someday someone will invent a car, which is basically the
| same thing." it's not. the limitations of horses are a
| feature of their biology and you are going to see
| diminishing returns on selectively breeding as you start to
| max out the capabilities of the horse's overall design, and
| while there certainly will be innovations in transportation
| in the future, the horse is not going to be a part of them.
| ForHackernews wrote:
| And that's more or less true? By 1989 certainly, we had
| word processors, spreadsheets, email, BBSes. We have
| _better_ versions of everything today, but fundamentally,
| yes, the "shape" of the personal computer was firmly
| established by the late 1980s.
|
| Anyone comfortable with MS-DOS Shell would not be totally
| lost on a modern desktop.
| ToucanLoucan wrote:
| The big missing thing between both the metaphor in the OP's
| link and yours is that I just can't fathom _any_ of these
| companies being able to raise a paying subscriber base that
| can actually cover the outrageous costs of this tech. It
| feels like a pipe dream.
|
| Putting aside that I fundamentally don't think AGI is in the
| tech tree of LLM, if you will, that there's no route from the
| latter to the former: even if there is, even if it takes, I
| dunno, ten years: I just don't think ChatGPT is a compelling
| enough product to fund about $70 billion in research costs.
| And sure, they aren't having to yet thanks to generous input
| from various commercial and private interests but like... if
| this is going to be a stable product _at some point,_
| analogous to something like AWS, doesn 't it have to...
| actually make some money?
|
| Like sure, I use ChatGPT now. I use the free version on their
| website and I have some fun with AI dungeon and occasionally
| use generative fill in Photoshop. I paid for AI dungeon (for
| awhile, until I realized their free models actually work
| better for how I like to play) but am now on the free
| version. I don't pay for ChatGPT's advanced models, because
| nothing I've seen in the trial makes it more compelling an
| offering than the free version. Adobe Firefly came to me free
| as an addon to my creative cloud subscription, but like, if
| Adobe increased the price, I'm not going to pay for it. I use
| it because they effectively gave it to me for free with my
| existing purchase. And I've played with Copilot a bit too,
| but honestly found it more annoying than useful and I'm
| certainly not paying for that either.
|
| And I realize I am not everyone and obviously there are
| people out there paying for it (I know a few in fact!) but is
| there enough of those people ready to swipe cards for...
| fancy autocomplete? Text generation? Like... this stuff is
| neat. And that's about where I put it for myself: "it's
| neat." OpenAI supposedly has 3.9 million subscribers right
| now, and if those people had to foot that 7 billion annual
| spend to continue development, that's about $150 a month.
| This product has to get a LOT, LOT better before I personally
| am ready to drop a tenth of that, let alone that much.
|
| And I realize this is all back-of-napkin math here but still:
| the expenses of these AI companies seem so completely out of
| step with anything approaching an actual paying user base, so
| hilariously outstripping even the investment they're getting
| from other established tech companies, that it makes me
| wonder how this is ever, ever going to make so much as a dime
| for all these investors.
|
| In contrast, I never had a similar question about cars, or
| AWS. The pitch of AWS makes perfect sense: you get a server
| to use on the internet for whatever purpose, and you don't
| have to build the thing, you don't need to handle HVAC or
| space, you don't need a last-mile internet connection to
| maintain, and if you need more compute or storage or
| whatever, you move a slider instead of having to pop a case
| open and install a new hard drive. That's absolutely a win
| and people will pay for it. Who's paying for AI and why?
| bhouston wrote:
| > The big missing thing between both the metaphor in the
| OP's link and yours is that I just can't fathom any of
| these companies being able to raise a paying subscriber
| base that can actually cover the outrageous costs of this
| tech. It feels like a pipe dream.
|
| I suspect these companies will introduce ads at some point
| similar to Google and Facebook for similar reasons, and it
| will be highly profitable.
| ToucanLoucan wrote:
| I mean that's quite an assertion given how the value of
| _existing_ digital ad space is already cratering and
| users are more than ever in open rebellion against ad
| supported services. And besides which, isn 't the whole
| selling point of AI to be an agent that accesses the
| internet and filters out the bullshit? So what, you're
| going to do that, then add your own bullshit to the
| output?
| WhyOhWhyQ wrote:
| I agree with everything you said in your previous post.
| The LLM as a search engine might basically eat Google's
| lunch in the same way the internet ate cable tv's lunch.
| Cable tv was a terrible product due to the advertising,
| and you could escape the BS by watching stuff online. Now
| look where we are.
| martin_drapeau wrote:
| The 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 wrote:
| But 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 wrote:
| There 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 wrote:
| Sure 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 wrote:
| I 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!"
| MOARDONGZPLZ wrote:
| You're moving the goalposts a little here. In your other
| post you implied you were using OpenAI for its technical
| properties. "But getting better and cheaper and faster over
| time."
|
| Whether something has more name recognition isn't
| completely related here. But if that's what you really
| mean, as you state, "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," then
| I mostly agree, but as an outsider it doesn't seem like
| this is a robust reason to build on top of.
| martin_drapeau wrote:
| OpenAI's sole focus is serving AI to consumers and
| businesses. I trust them more to remain backwards
| compatible over time.
|
| Google changed their AI name multiple times. I've built
| on them before and they end of lifed the product I was
| using. Zero confidence Gemini will be there tomorrow.
| jfoster wrote:
| That's like saying "how do you monetize the internet?"
|
| There are so many ways, it makes the question seem nonsensical.
|
| Ways to monetize AI so far:
|
| Metered APIs (OpenAI and others)
|
| Subscription products built on it (Copilot, ChatGPT, etc.)
|
| Using it as a feature to give products a competitive edge
| (Apple Intelligence, Tesla FSD)
|
| Selling the hardware (Nvidia)
| martin_drapeau wrote:
| 20 years ago people asked that exact question. E-Commerce
| emerged. People knew the physical process of buying things
| would move online. Took some time. Sure, more things emerged
| but monetizing the Internet still remains about selling you
| something.
|
| What similar parallel can we think of for AI?
| YetAnotherNick wrote:
| Assuming AI progress continues, AI could replace both
| Microsoft's biggest product, OS, and Google's biggest
| product, search and ads. And there is a huge tail end of
| things autonomous driving/flying, drug discovery, robotics,
| programming, healthcare etc.
| adamc wrote:
| Too vague. How would it replace Windows? How would it
| replace search?
|
| The latter is more believable to me, but how would the
| AI-enhanced version generate the additional revenue that
| its costs require? And I think a lot of improvement would
| be required... people are going to be annoyed by things
| like hallucinations while trying to buy products.
|
| In reality, as soon as a competitor shows up, Google will
| add whatever copycat features it needs to search. So it
| isn't clear to me that search is a market that can be
| won, short of antitrust making changes.
| WhyOhWhyQ wrote:
| They'll be selling overpriced licenses per computer to every
| fortune 500 company.
| overcast wrote:
| My guess would be using "AI" to increase/enhance sales with
| your existing processes. Pay for this product, get 20%
| increased sales, ad revenue, yada yada.
| skeeter2020 wrote:
| I don't see how you charge enough for the second path to make
| the economics work.
| janoc wrote:
| The 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 wrote:
| No, clearly, AIs do all the work for them.
| bradhilton wrote:
| Kind 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 wrote:
| It took 15 years for Uber to turn a profit. Will AI investors
| have that much patience?
| dj_axl wrote:
| You're perhaps forgetting Waymo at $30B
| marcosdumay wrote:
| Is 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.
| SoftTalker wrote:
| Uber the company was profitable last year, for the first
| time[1].
|
| But I am doubtful that the larger enterprise that is Uber
| (including all the drivers and their expenses and vehicle
| depreciation, etc) was profitable. I haven't seen that
| analysis.
|
| [1] https://www.theverge.com/2024/2/8/24065999/uber-earnings-
| pro...
| kridsdale1 wrote:
| Because they put ads on their UI.
| torginus wrote:
| Uber is not really a tech company though - its moat is not
| technology but market domination. If it, along with all of its
| competitors were to disappear tomorrow, the power vacuum would
| be filled in very short order, as the core technology is not
| very hard to master.
|
| It's a fundamentally different beast from AI companies.
| VirusNewbie wrote:
| How is it not a tech company? They're literally trying to
| approximate TSP in the way that makes them money. In
| addition, they're constantly optimizing for surge pricing to
| maximize ROI. What kind of problems do you think those are?
| Apocryphon wrote:
| A good meditation on that:
| https://www.readmargins.com/p/-the-zombie-apocalypse-scale
|
| > I call this scale the Zombie Apocalypse Scale. It is a
| measure of how many days a company could run for it all its
| employees were turned to zombies overnight. The more days
| you can operate without any humans the more of a tech
| company you are.
| matchagaucho wrote:
| _> 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 wrote:
| I 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 wrote:
| "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.
| Alex-Programs wrote:
| Just the fact that I can have something proficient in
| language trivially accessible to me is really useful. I'm
| working on something that uses LLMs (language translation),
| but besides that I think it's brilliant that I can just ask
| an LLM to summarise my prompt in a way that gets the point
| across in far fewer tokens. When I forget a word, I can give
| it a vague description and it'll find it. I'm terrible at
| writing emails, and I can just ask it to point out all the
| little formalisms I need to add to make it "proper".
|
| I can benchmark the quality of one LLM's translation by
| asking another to critique it. It's not infallible, but the
| ability to chat with a multilingual agent is brilliant.
|
| It's a new tool in the toolbox, one that we haven't had in
| our seventy years of working on computers, and we have
| seventy years of catchup to do working out where we can apply
| them.
|
| It's also just such a radical departure from what computers
| are "meant" to be good at. They're bad at mathematics,
| forgetful, imprecise, and yet they're incredible at poetry
| and soft tasks.
|
| Oh - and they are genuinely useful for studying, too. My A
| Level Physics contained a lot of multiple choice questions,
| which were specifically designed to catch people out on
| incorrect intuitions and had no mark scheme beyond which
| answer was correct. I could just give gpt-4o a photo of the
| practice paper and it'd tell me not just the correct answer
| (which I already knew), but why it was correct, and precisely
| where my mental model was incorrect.
|
| Sure, I could've asked my teacher, and sometimes I did. But
| she's busy with twenty other students. If everyone asked for
| help with every little problem she'd be unable to do anything
| else. But LLMs have infinite patience, and no guilt for
| asking stupid questions!
| skydhash wrote:
| > _I can benchmark the quality of one LLM 's translation by
| asking another to critique it_
|
| Do you speak two or more languages? Anyone that does is
| wary of automated translations, especially across estranged
| cultures.
|
| > _It 's a new tool in the toolbox, one that we haven't had
| in our seventy years of working on computers,_
|
| It's data analysis at scale, and reliant on scrapping what
| humans produced. A word processor does not need TB of
| eBooks to do it's job.
|
| > _They 're bad at mathematics, forgetful, imprecise, and
| yet they're incredible at poetry and soft tasks._
|
| Because there's no wrong or right about poetry. Would you
| be comfortable having LLMs managing your bank account?
|
| > _If everyone asked for help with every little problem she
| 'd be unable to do anything else._
|
| That would be hand-holding, not learning.
| tene80i wrote:
| What was the demo? And what is the advantage of integrating
| local LLMs vs third party?
| troupo wrote:
| > In my mind, already with GPT-4, we're not generating ideas
| fast enough on how best to leverage it.
|
| It's a token prediction machine. We've already generated most
| of the ideas for it, and hardly any of them work because see
| below
|
| > 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.
|
| No. Gettin AI to work you need to make an AI, and not a token
| prediction machine which, however wonderful:
|
| - does not understand what it is it's generating, and
| approaches generating code the same way it approaches
| generating haikus
|
| - hallucinates and generates invalid data
|
| > 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"
|
| Indeed. Instead of asking why, you're wildly fantasizing about
| running out of ideas and pretending you can make this work
| through other means of communication.
| yibg wrote:
| * We're seeing much less of "it's making mistakes" these days.*
|
| Perhaps less than before, but still making very fundamental
| errors. Anything involving number I'm automatically suspicious.
| Pretty frequently I'd get different answers for the same
| question (to a human).
|
| e.g. ChatGPT will give an effective tax rate of n for some
| income amount. Then when asked to break down the calculation
| will come up with an effective tax rate of m instead. When
| asked how much tax is owed on that income will come up with a
| different number such that the effective rate is not n or m.
|
| Until this is addressed to a sufficient degree, it seems
| difficult to apply to anything that involves numbers and can't
| be quickly verified by a human.
| elorant wrote:
| We have ideas on how to leverage it. But we keep them to
| ourselves for our products and our companies. AI by itself
| isn't a breakthrough product the same way that the iPhone or
| the web was. It's a utility for others to enhance their
| products or their operations. Which is the main reason why so
| many people believe we're in an AI bubble. We just don't see
| the killer feature that justifies all that spending.
| shahzaibmushtaq wrote:
| Yes, 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 wrote:
| Why would this make them work?
| culebron21 wrote:
| I 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 wrote:
| So 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 wrote:
| That's Netflix's revenue for a single quarter, not year.
| (nonetheless, your point is relevant)
| arbuge wrote:
| > 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 wrote:
| I 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 wrote:
| Two 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 wrote:
| TSMC is by large the biggest chip company of the world and they
| are still investing lots of money in research.
| cutemonster wrote:
| > 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 wrote:
| This 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.
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