[HN Gopher] Investors are happy to pay premium for tech, but not...
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Investors are happy to pay premium for tech, but not for AI
Author : alex1212
Score : 111 points
Date : 2023-07-31 14:48 UTC (8 hours ago)
(HTM) web link (www.bloomberg.com)
(TXT) w3m dump (www.bloomberg.com)
| rvz wrote:
| Investors already know that this is a race to zero. There are
| some companies in tech that are already at the finish line in
| this race, like Meta and can afford to release their AI model for
| free, undercutting cloud based AI models unless they also do the
| same.
|
| They are also realizing that the many of these new 'AI startups'
| using ChatGPT or a similar AI service as their 'product' are a
| prompt away from being copied or duplicated.
|
| The moat is quickly getting evaporated by $0 free AI models. All
| that needs to happen is for these models to be shrunken down and
| be better than the previous generation whilst still being
| available for free.
|
| Whoever owns a model close to that is winning or has already won
| the race to zero.
| twelve40 wrote:
| I've seen some hype waves in my life, but it's probably the first
| one that truly unleashed the sleazy "influencer" types that
| regurgitate the same carousels they steal from each other. Even
| more intense than "crypto" now. That really kind of distracts
| from trying to gauge the meaning of this.
| akokanka wrote:
| Are investors still in cyber security startups or is that train
| long gone?
| alex1212 wrote:
| I dont think so but compliance seems to be getting hot. Ryan
| Hoover listed "comply or die" as a hot space in his thesis
| recently.
| happytiger wrote:
| This is explained by the simple idea that only a few companies
| are in an arms race to create a general purpose intelligence, and
| when they do all of the ai-powered systems will naturally
| consolidate or "become flavors" of this GPI AI.
|
| So what substantive and defensible advantage is your money buying
| in the AI ethos when this effect is essentially inevitable?
|
| Answer: not much.
|
| So it's very logical that the team, book of business and the tech
| platform itself are what are driving valuations.
| zby wrote:
| I have the feeling that we are at the MRP stage
| (https://en.wikipedia.org/wiki/Material_requirements_planning)
| when companies started using computers but writing software to
| handle production processes was so new that nobody could write
| anything truly universal. The next will be the ERP stage where we
| know some abstractions that apply to many companies, companies
| like SAP can sell some software - but most money is in
| 'implementation' by consulting agencies.
| nkohari wrote:
| So many AI startups are really just paper-thin layers over
| publicly-available models like GPT. There's value there, but
| probably not enough to support $100M+ valuations.
|
| We've barely scratched the surface of what generative AI can do
| from a product perspective, but there's a mad dash to build
| "chatbots for $x vertical" and investors _should_ be a little
| skeptical.
| coffeebeqn wrote:
| I'm fairly certain some are just prompt prefixes. Maybe a
| lookup to some 1st or 3rd party dataset
| qaq wrote:
| So many startups are just a paper-thin layers over publicly-
| available AWS services.
| nkohari wrote:
| The developer experience of AWS is so bad it creates a lot of
| opportunity to provide value there. The same was also true
| for Salesforce for a long time.
| lispisok wrote:
| Some other company's LLM being your secret sauce is different
| than using AWS services to build your secret sauce on top of.
| coding123 wrote:
| That's my take too. Companies are spending money in the wrong
| area. GPT and similar should be used to re-categorize all the
| data, or used to enhance their existing UIs by surfacing
| related information.
|
| By just replicating a ChatGPT interface but for Your Taxes (TM)
| it's really a huge slap in the face to computer users that
| already can't tolerate typing data in.
| tamimio wrote:
| Good, I'm not the only one who's getting fed up with yet-another-
| chatbot or chat-with-your-files or whatever "startup".
| alex1212 wrote:
| No, we all are. Getting super tired by anything in the space
| rsynnott wrote:
| I feel like these hype cycles are getting quicker and quicker.
|
| 2030, day 8 of the 17th AI boom: A starry-eyed founder shows up
| to a VC office with a pitch-deck for their GPT-47-based startup
| which automatically responds to Yelp reviews, only to be turned
| away; the VCs are done with that now, and will be doing robot
| dogs for the next week.
| twobitshifter wrote:
| I wonder how much of AI will be winner take all and how much will
| be value destruction. From an investor standpoint in LLM you have
| a privately held business leading the market and open source
| software following closely.
|
| During the PC revolution you could buy apple hp and Microsoft and
| know that you were capturing the hardware market. Here we see
| Nvidia, AMD, Apple, and Microsoft (somewhat) looking like the
| major beneficiaries and the market is following that. Maybe it
| becomes a Omni-platform market and people rush into OpenAI once
| public.
| jasfi wrote:
| This is healthy skepticism and the acknowledgement that there are
| lots of free tools out there. You need to be much better than
| what's freely available. You need to persuade buyers to buy when
| they don't want to. I don't think any of that is new.
| rmbyrro wrote:
| http://web.archive.org/web/20230731171759/https://www.bloomb...
| reilly3000 wrote:
| What I have heard from YC folks was that typically the hard costs
| (GPU compute) and data moats of large players make the space
| virtually impossible for an upstart to make a meaningful
| difference that isn't immediately copied wholesale by a major
| player.
|
| Software velocity is increasing. Investors should be considering
| what that means for their investments.
|
| I would be worried if I were tied up in a company that depends on
| bloated professional services. LLM-enabled senior engineers are
| 100X more efficient and safe than brand new junior devs. These
| organizations that embrace the best people using the best tech
| ought to make Oracle and their famous billion dollar cost
| overruns quake in their boots.
| vasili111 wrote:
| While I think that some AI startups and new AI products will be
| successful I also think that from AI revolution mostly will
| benefit companies that will integrate new AI technologies in
| their existing product.
| gpvos wrote:
| https://archive.ph/qwSAH
| codegeek wrote:
| Seems like Investors are cautious and not getting on the hype
| train blindly (cough.. crypto/blockchain cough..). I think that
| is a good thing. AI has real use cases but currently it is going
| through the hype cycle especially with every Tom Dick and Harry
| starting an "AI Startup" which are mostly a wrapper around
| ChatGPT etc. I think in next 5-7 years, AI will stabilize and
| most of the "get rich quick" types would have disappeared.
| Whatever is left then will be the AI and its future.
| mach1ne wrote:
| Depends on what you mean by 'wrapper'. For most AI startups it
| isn't viable to train their own models. For most customer use-
| cases, ChatGPT interface isn't enough. Wrappers are currently
| the only logical implementation of AI to production.
| ska wrote:
| This is approximately true at the moment - but it's an open
| question how much that is worth to customers. The market will
| sort it out, but it's not clear that all of these "wrapper"
| startups have a workable business model.
| mach1ne wrote:
| True, especially regarding how easily their services can be
| replicated. Their margins are low, and customer acquisition
| does not provide them with network effects that would yield
| a moat.
| soulofmischief wrote:
| ha ha, another "cryptocurrency has no real use cases but <new
| thing I am excited about> does" post on HN, my favorite meme.
| wiseowise wrote:
| It's true, though.
| soulofmischief wrote:
| The extreme irony is that the automated web will largely be
| used by AI to begin with, and the automated web is powered
| by decentralized computational efforts such as smart
| contracts and digital currency. It's like people completely
| forget /ignore that cryptocurrency is a mathematical
| problem still in its infancy.
|
| If you think these aren't all fundamental units of the next
| web, you're not thinking about it from the right
| perspective. If you can't pick apart the real mathematical
| utility behind crypto efforts from a generation of scammers
| who hijacked a very real thing, then you just lack
| understanding.
|
| We are decades away from the most obvious solution but it
| very likely involves cryptographically-backed digital
| currency and smart contract systems used by automated
| neural networks.
| strangattractor wrote:
| That is new in itself. When have VC's ever not jumped on the
| hype wagon? The lemmings squad has FOMO for blood.
| dehrmann wrote:
| We might be on a hype train, but ChatGPT is already much more
| useful than bitcoin ever was.
| codegeek wrote:
| I agree with you there.
| LordDragonfang wrote:
| I think that's precisely why the investors aren't as
| interested - bitcoin had very little value by itself, so
| investors got dollar signs in their eyes when a startup
| claimed to be able to _add_ the value it was missing.
|
| ChatGPT already has a _lot_ of value by itself, the value
| _added_ by any startup is going to be marginal at best.
| boredumb wrote:
| This is absolutely correct. As soon as I was able to get
| access I built my own... GPT proxy to generate marketing
| copy and all that for people and while it was neat it comes
| down to a regular crud application that has a wrapper
| around an OpenAI API, the moat isn't there, the app was
| alright but I realized pretty quickly my "value" being that
| I'm basically using a template engine against a text prompt
| - I probably shouldn't shut down my consulting business
| over pursuing it.
| tsunamifury wrote:
| I think this is a good example of the VC mindset, but I
| think it is also flawed on their part.
|
| LLMs are a lot more like a generalized processor than
| people are admitting right now. Granted you can talk to it,
| but it becomes significantly more capable when you learn
| how to program it -- and thats where the value will be
| added.
| LordDragonfang wrote:
| >when you learn how to program it
|
| I don't know if you mean, like, LoRAs and similar (actual
| substantive changes), but the vast majority of "learning
| how to program" LLMs (accounting for the majority of
| startup pitches as well) is "prompt engineering" - which,
| as the meme goes, isn't a moat. There's a skill to it,
| yes, but if your singular advantage boils down to a few
| lines of English prose, your product isn't able to
| control a market - and VCs are (rightly) not interested
| unless you have the possibility to be a near-monopoly.
| svnt wrote:
| > I don't know if you mean, like, <extensions> and
| similar (actual substantive changes), but the vast
| majority of "learning how to program" <computers>
| (accounting for the majority of startup pitches as well)
| is "<punchcard> engineering" - which, as the meme goes,
| isn't a moat. There's a skill to it, yes, but if your
| singular advantage boils down to a few lines of <clever
| pointer dereferences>, your product isn't able to control
| a market - and VCs are (rightly) not interested unless
| you have the possibility to be a near-monopoly.
| tsunamifury wrote:
| This is the error of the thinking. It would be like
| saying software doesn't have a moat because thats just
| clever talking to a processor.
|
| But no one would say that now, thats ridiculous. There is
| a sufficient degree of prompt engineering that is already
| defensible, I'm already doing it myself IMO. You'll see
| very sophisticated hybrid programming/prompting systems
| being developed in the next year that will prove out the
| case.
|
| For example 30 parallel prompts that then amalgamate into
| a decision and an audit, with 10 simulation level prompts
| running chained afterwards to clean the output. These
| types of atomic configurations will become sufficiently
| complex to not be just for 'anybody'.
| __loam wrote:
| The output of that many chained prompts is probably so
| unreliable that it's useless.
| tsunamifury wrote:
| Again, this is a misunderstanding of what LLMs are
| capable of. These aren't chained, you can run parallel
| prompts of 15 personas with X diversity of perspective,
| that reason on a singular request, string, input or
| variable, they provide output plus audit explanation. You
| then run an amalgamation or committee decision (sort of
| like mixture of experts) on it to output variable or
| string. Then run parallel simulation or reflection
| prompts based on X different context personas to double
| check their application to outside cases, unconsidered
| context, etc.
|
| It's pretty effective on complex problems like Spam,
| Trust and Safety, etc. And the applications of these sort
| of reasoning atomic configurations I think are unlimited.
| It's not just 'talking fancy' to an AI, its building
| processes that systematically improve reasoning to
| different very hard applied problems.
| ironborn123 wrote:
| Brave are those who have set out to make prompt engg an
| entire industry, with gpt-5 and gemini lurking on the
| horizon.
| tsunamifury wrote:
| Sam has specifically stated it's unlikely there will be a
| GPT5, and likely GPT4 is just a deeply prompt optimized
| and multimode version of GPT3.
|
| But overall, hasn't that theme been true for like... all
| tech ever? You have to set up and build your own
| innovation path at some point.
| matmulbro wrote:
| [flagged]
| dlkf wrote:
| > the applications of these sort of reasoning atomic
| configurations I think are unlimited
|
| They are limited to applications in which the latency slo
| is O(seconds), knowledge of 2021-present doesn't matter,
| and you're allowed to make things up when you don't know
| the answer.
|
| There are, to be fair, many such applications. But it's
| not unlimited.
| [deleted]
| EA-3167 wrote:
| I think people are starting to realize that "AI" in the present
| context is just the new vehicle for people who were yelling,
| "NFT's and Cyrpto" just a year ago.
| xwdv wrote:
| I can't wait for these people to run out of "vehicles" and
| face the reality.
| bushbaba wrote:
| I think it's more to do with high rate environment, with most
| AI firms having no clear path to profitability. Where-as many
| traditional tech venture rounds (now of days) have a solid
| business model and current profitability per deal, using raised
| capital to accelerate growth at current loss for long term
| profit.
| ben_w wrote:
| Even with my rose-tinted glasses on about the future of AI,
| it's not clear who will be the "winner" here, or even if any
| business making them will be a winner.
|
| If open source models are good enough (within the category of
| image generators it looks like many Stable Diffusion clone
| models are), what's the business case for Stability AI or
| Midjourney Inc.?
|
| Same for OpenAI and LLMs -- even though for now they have the
| hardware edge and a useful RLHF training set from all the
| ChatGPT users giving thumbs up/down responses, that's not
| necessarily enough to make an investor happy.
| rebeccaskinner wrote:
| Early signals to me are that regulatory capture will end up
| being the moat that gets used here. I think it's a horrible
| outcome for society, but likely one that will make some
| companies a lot of money. Early grumblings around regulation
| for a lot of AI models seem at risk of making open source
| models (and even open-weigh models) effectively illegal.
| Training from scratch is also going to both remain
| prohibitively expensive for individuals and most bootstrapped
| startups, plus with more of the common sources of data
| locking out companies from using training data it's going to
| be hard for new entrants to catch up.
|
| I personally think the only way AI will end up being a
| benefit to society is if we end up with unencumbered free and
| open models that run locally and can be refined locally.
| Every financial incentive is pushing in the other direction
| though.
| benreesman wrote:
| This should be one of the highest voted comments in all of
| the AI threads this year.
|
| Meta is no doubt doing this because it's in their best
| interest, but if both the quality and licensing of LLaMA 2
| start a trend that's a pretty effective counter-weight to
| eyeball scanner world.
|
| And there's other stuff. George Hotz is pretty unpopular
| because he does kind of put the crazy in crazy smart (which
| I personally find a refreshing change to the safe space for
| relatively neurotypical people in the land of aspy nerds),
| but tinygrad is a fundamentally more optimizable design
| than its predecessors with an explicit technical emphasis
| on accelerator portability and an implicit idealistic
| agenda around ruining the whole day of The AI Cartel. And
| it runs the marquee models. Serious megacorp CEOs seem to
| be glancing nervously in his direction, which is healthy.
|
| It's not locked-in yet.
| serjester wrote:
| Agreed look at Jasper - a year ago the 600lb gorilla in the
| room and overnight their moat has dried up along with many of
| their customers.
| mahathu wrote:
| I know nothing about AI or stocks, so please correct me if
| I'm wrong here: Isn't NVIDIA a clear winner already (bar any
| major technological advances allowing all of us to run LLMs
| on our phones?) I just checked the stock on google and it
| went up 200% since the beginning of the year!
| emadm wrote:
| Make models usable is really valuable, for Stability AI I
| discussed business models with Sam Lessin here:
| https://www.youtube.com/watch?v=mOOYJONenWU but basically the
| edge is data and distribution given how widely used this
| technology will be.
|
| Open is its own area, proprietary general models are a race
| to zero vs OpenAI and Google who are non-economic actors.
|
| Most AI right now is just features tho, very basic without
| the real thinking needed.
|
| Next year we go enterprise.
| alex1212 wrote:
| Definitely a hype cycle at the moment. I am old enough that
| this is my second ;)
| hattmall wrote:
| Second hype cycle or second AI hype cycle? If the latter when
| was the first?
| ska wrote:
| Wikipedia has a summary of some of the earlier history here
| https://en.wikipedia.org/wiki/AI_winter
| dspillett wrote:
| Not the OP, but I've been through a few AI hype cycles and
| know of earlier ones, depending on what you count: ML more
| generally over the last half decade or so1, the excitement
| around Watson (and Deep Blue before it), the second big
| bump in neural network interest in the mid/late 80s2, there
| have been a couple of cycles regarding expert-system-like
| methods over the decades, etc.
|
| --
|
| [1] though that has produced more useful output than some
| of the previous hype cycles, as I think will the current
| one as it seemingly already is doing
|
| [2] I was barely born for the start of the "AI winter"
| following the first such hype cycle
| yxre wrote:
| 1955 with the advent of the field with some very hopeful
| mathematicians, but the research never produced anything.
|
| 1980 after the foundations of neural networks, but it was
| too computationally intensive to be useful
|
| 2009 with Watson
|
| https://www.hiig.de/en/a-brief-history-of-ai-ai-in-the-
| hype-...
| padolsey wrote:
| Could this time be different? The tools are now in the
| hands of the "masses", not behind closed doors or in
| lofty ivory towers. People can run this stuff on their
| laptops etc
| sgift wrote:
| Could yes? Will it be? I can tell you when you don't need
| the answer anymore, i.e. in a few years.
|
| It's the very nature of the hype cycle that it is very
| hard to distinguish from a real thing.
| timy2shoes wrote:
| Every time someone says "this time it's different" (e.g.
| 1998 internet bubble, 2007 housing bubble, 2020 crypto
| bubble, etc) time proves that this time was not really
| that different.
| jsight wrote:
| But the internet did change things? Even crypto is
| debatable. BTC still exists and still has pretty high
| value, just not as high as at its peak.
|
| TBH, I'm not sure how to quantify housing bubbles either.
| I'd bet most of the country has much higher home prices
| now than in 2007. I bet they were higher than 2007 in
| most places and most years between then and now too.
| civilitty wrote:
| _> (e.g. 1998 internet bubble, 2007 housing bubble, 2020
| crypto bubble, etc)_
|
| That's some extreme cherry picking.
|
| During that time period, the internet and smartphones
| alone have completely changed society (for better and
| worse) in the span of only three decades, despite the
| former going causing a minor economic crash in its
| infancy.
|
| Almost everything _is_ different except human nature. The
| scammers are innovating just like everyone else.
| goatlover wrote:
| When someone says a technology completely changed
| society, I think of the hypothetical singularity that
| Kurzweil and company predict, where it's basically
| impossible for us to predict what the future looks like
| after. But when you look back at the world before the
| rise of the web and then smartphones, it's just taking
| preexisting technologies and making them available in
| more mobile formats. TV, radio, satellite and computers
| existed before then (1968 mother of all demos had word
| processing, hypertext, networking, online video). And
| some people did more or less foresee what we've done
| online since.
|
| We still burn fossil fuels to a large extent, still drive
| but not fly cars, still live on Earth not in space, still
| die of the same causes, etc.
|
| I watch a long cargo train that looks like it's form the
| 80s go by and wonder how much the internet changed cargo
| hauling. I'm sure with the logistics the internet made
| things a lot more efficient, but the actual hauling is
| not much different. It's not like we teleport things
| around now. You can order online instead of out of a
| catalog, but brick stores remain. You can read digital
| books, but still plenty of printed materials, bookstores,
| libraries.
| JohnFen wrote:
| > the internet and smartphones alone have completely
| changed society
|
| I honestly think this overstates the case pretty
| severely. They have certainly caused societal change, but
| from what I can see, society as a whole is not actually
| all that different from what it was before all of that.
| vikramkr wrote:
| Well, of those 3 the 1998 internet bubble actually was
| different and modern society actually was fundamentally
| changed by the technology in question, so idk if that's
| the best counterargument. The other two, sure yeah those
| amounted to essentially nothing. But there have been
| plenty of bubbles where the concept underlying the bubble
| actually did have large societal impacts even if the
| investors all lost money, like tulipmania with futures
| contracts and railway mania with trains
| [deleted]
| p1esk wrote:
| I don't remember much happening with NNs in 1980. There
| was a lot of hype in 1992-1998 though.
| rsynnott wrote:
| There was also a voice recognition thing in the 90s, the
| whole self-driving car/computer vision thing early to mid
| last decade, and a _very_ short-lived period when
| everyone was a chatbot startup in 2016 (I think Microsoft
| Tay just poured so much cold water over this that it died
| almost immediately).
| alex1212 wrote:
| Spot on, 2009 with Watson was my first. Oh, the
| memories...It was nowhere near as nuts as this one, at
| least in my head.
| paulddraper wrote:
| Machine learning became quite the buzzword in 2016(?)
| alex1212 wrote:
| I definitely remember it being the "hot thing" in the ad
| tech space
| ben_w wrote:
| I can remember hyped up news after Watson; personally I was
| hyped up after Creatures (though in my defence I was a
| teenager and hadn't really encountered non-fictional AI
| before); before those there was famously the AI winter,
| following hype that turned out to be unreasonable.
| jvanderbot wrote:
| Fourth here, if you count dot com, early robotics cum self
| driving cars, and web 3. Each had their impact, winners and
| vast array of losers.
| dadoomer wrote:
| According to the article 38% see a correction in the near-ish
| future.
|
| Also,
|
| > Unlike during the dot-com bubble of the 2000s, AI isn't
| entirely based on speculation.
|
| I'd say the dot-com bubble was backed by a revolutionary product:
| the Internet. That doesn't change that expectations were too
| high.
| sebzim4500 wrote:
| Were expectations too high?
|
| Some of the companies involved are now worth trillions.
| rsynnott wrote:
| Amazon, okay, but who else? Nearly all of the big .com-era
| startups (and major non-startup beneficiaries, like Sun) are
| _gone_. Yahoo somehow still exists, I suppose.
|
| I suppose you could argue Google, but it's an odd one; it was
| right at the tail end, and was really only taking off as
| everything else was collapsing
| hollerith wrote:
| I would argue Google because the dot-com bubble did not
| burst till 2000, and Google was founded in 1998.
| robryan wrote:
| Probably the same with AI, the short term hype cycle being
| too early for the vast majority of companies.
| dudeinhawaii wrote:
| You really have to have your own existing moat for AI to augment
| (ala Adobe, Microsoft, etc). Anything built directly on AI can be
| replicated rather quickly once someone figures out what
| combination of prompt + extra data was used.
|
| That said, you don't have to be the mega players to have an
| existing small moat. If your product does something great
| already, you get to improve it and add value for users very
| quickly. That's been my experience anyway.
| caesil wrote:
| "once someone figures out what combination of prompt + extra
| data was used"
|
| This is assuming your thing is one call to GPT-n rather than a
| complex app with many LLM-core functions, and it also assumes
| that data is easy to get.
| spamizbad wrote:
| For people who are in AI companies or have heard their pitches:
| What's the typical response to "What makes your AI special that
| can't be replicated by a dozen competitors?"
| paulddraper wrote:
| Everything can be replicated with time and money.
|
| All the usual things.
|
| First mover
|
| Features
|
| Integrations
|
| Platform synergies
| dgb23 wrote:
| A lot of it is UX and molding things to specific domains and
| use cases.
|
| Just like forms over SQL, there seems to be a never ending
| demand.
| claytonjy wrote:
| Is Jasper a counterexample? Good UX, domain-specific, but
| still a standalone ChatGPT wrapper forced to do layoffs
| because they have no moat.
| alex1212 wrote:
| From memory, they raised a huge round just before chat gpt
| went viral. Not sure if they would have been able to do so
| well if they were raising now. Very much doubt it.
| yujian wrote:
| I work on Milvus at Zilliz and we encounter people working on
| LLM companies or frameworks often, I don't ask this question a
| lot a lot, but it looks like at the moment many companies don't
| have a real moat, they are just building as fast as they can
| and using talent/execution/funding as their moat
|
| I've also heard some companies that build the LLMs say that
| those LLMs are their moat, the time, money, and research that
| goes into them is high
| version_five wrote:
| I've sold various "AI" consulting projects, I tell people that
| all the AI hard- tech is open source and that there's nothing
| that differentiates it. What is different is implementation
| experience and industry customization. For example everyone has
| datasets scraped from the internet, but there are not deep
| application specific datasets publicly available. Likewise
| experience with the workflows in an industry.
|
| It's just software, there's little "secret sauce" in the
| engineering, it's the knowledge of the customer problem that's
| the differentiator.
| PaulHoule wrote:
| I worked at a place where we thought there was value into
| putting it together in one neat package with a bow.
|
| That is, a lot of people are thinking at the level of "let's
| build a model" but for a business you will need to build a
| model and then update it repeatedly with new data as the
| world changes and your requirement changes.
|
| There would be a lot to say for a solution that includes
| tools for managing training sets, foundation models, training
| and evaluation, packages stuff up for inference in a
| repeatable way, etc.
|
| One trouble though is that you have to make about 20
| decisions or so about how you do those things and developing
| that kind of framework people get some of them wrong and it
| will drive you crazy because other people will make different
| wrong decisions than you will. (To take an example, look at
| the model selection tools in scikit-learn and huggingface.
| Both of these are pretty good for certain things but they
| don't work together and both have serious flaws... And don't
| get me started with all the people who are hung up on F1 when
| they really should be using AUC...)
|
| So given the choice of (a) building out something half baked
| vs (b) fighting with various deficiencies in a packaged
| system, you can't blame people for picking (a) and "Just
| doing it". (Funny enough I always told people at that startup
| that we'd get bought by one of our customers, I thought it
| was going to be a big four accounting firm, a big telecom, or
| an international aerospace firm but... it turned out to be a
| famous shoe and clothing brand.)
| alex1212 wrote:
| We focus on "selling" the market size, customer problem-
| solution fit and not so much the AI part. AI is just the means
| to an end, a better way to solve the problem that we are
| solving. I saw some interesting stats the other day that the
| majority of investments in AI focus on infrastructure
| (databases etc) and foundational models.
| chriskanan wrote:
| Having large amounts of curated data that is hard to procure,
| e.g., medical imaging data.
|
| If one can scrape the data from the web, I can't imagine having
| much of a moat or selling point.
| alex1212 wrote:
| Its tricky because by definition the more use-case specific
| the data the harder to obtain at scale, with some exceptions.
| claytonjy wrote:
| 1. research talent. There's not actually that many people in
| the world that can adequately fine-tune a large cutting-edge
| model, and far fewer that can explore less mainstream paths to
| produce value from models. Only way to get good researchers is
| to have name-brand leaders, like a top ML professor.
|
| 2. data. Can't do anything custom without good training data!
| How to get this varies widely across industry. Partnerships
| with established non-tech companies are a common path, which
| tend to rely on the network and background of founders.
|
| Even with both those things it's not easy to outcompete a
| large, motivated company in the same space, like a FAANG. They
| have the researchers, they have the data and partnerships, so
| the way to beat them is to move quickly and hope their A- and
| B-teams are working on something else.
| bugglebeetle wrote:
| > There's not actually that many people in the world that can
| adequately fine-tune a large cutting-edge model
|
| If you know how to run a Python script, you can fine-tune a
| LLama model:
|
| https://huggingface.co/blog/llama2#fine-tuning-with-peft
| polygamous_bat wrote:
| That's roughly akin to saying "if you have a wrench, you
| can fix a car", and posting a link to a YouTube tutorial
| with it.
| bugglebeetle wrote:
| No, not really. The script does the vast majority of the
| work. The only challenges here would be knowing how to
| use Google Colab and formatting/splitting your training
| and test data. That's the computer science equivalent of
| adding wiper fluid to your car.
| claytonjy wrote:
| It's true we've made this easy, and that's awesome, but
| this is not what AI startups do; this is what other
| companies experimenting with AI do.
| bugglebeetle wrote:
| Ok, but that's goalpost shifting. We've gone from there
| are not many people who can fine-tune a model
| (demonstrably untrue) to there are not many people who
| can do {???} that AI startups do. It's unclear what is
| this special AI startup thing you're referring to, but
| given that various fine-tuning strategies, like QLORA,
| emerged out of the open source community, this also seems
| unlikely to be true.
| claytonjy wrote:
| Yeah, that's fair. I could have been more precise about
| what an advantage research talent can be.
|
| As an example, the startup-employed AI researchers I know
| had already PEFT'd llama2 within a day or two of the
| weights being out, determined that wasn't good enough for
| their needs, and began a deeper fine tuning effort.
| That's not something I can do, nor can most people, and
| it's a serious competitive advantage for those who can.
| It's a rather different interpretation of "can adequately
| fine-tune" than "can follow a tutorial".
|
| When I think "AI startup", I think of the places where
| these people work. I don't think there's many of those
| people, and I think their presence is a big competitive
| advantage for their employers.
| bugglebeetle wrote:
| Understood. Apologies, I wasn't trying to be combative. I
| agree that what you describe requires a special emphasis
| on AI stuff or at least a part of the org that has a
| research focus. I work on an R&D team at a legacy org and
| we do the latter.
| alex1212 wrote:
| Research / talent is how Mistral was able to justify its
| valuation at 3 weeks old. Pre product, pre anything.
| claytonjy wrote:
| Yes, totally. Not something anyone reading this is going to
| replicate!
| Palmik wrote:
| > Not something anyone reading this is going to
| replicate!
|
| They were L7/L6 ML researchers/eng at FAANG, I'd bet
| there are quite a few people like that lurking here.
| claytonjy wrote:
| I think there's rather more to it than that. Two of these
| guys are on the Llama paper; the hype and momentum from
| that is surely responsible for a huge chunk of their
| valuation. If you take the big LLM-relevant papers, most
| of the folks with this kind of profile are already off
| doing some kind of startup.
|
| The Mistral folks have impeccable timing, but are leaving
| FAANG somewhat late compared to their peers.
| Palmik wrote:
| Definitely. Just to be clear, my comment wasn't to
| diminish the Mistral folks, they are certainly a very
| impressive group, but rather to contest your implication
| about the audience here.
| alex1212 wrote:
| Interestingly enough I think there is lack of talent on
| the investment side of things too. Very few investors
| have the right skillsets in their teams to be able to do
| deep technical due diligence required for true AI
| solutions.
| rvz wrote:
| > "What makes your AI special that can't be replicated by a
| dozen competitors?"
|
| As you can see with all the responses here, they have failed to
| realize that this is a trick question.
|
| The real answer is that _none_ are special and can be
| replicated by tons of competitors.
| PaulHoule wrote:
| (1) In the current environment things are moving so fast that
| the model of "get VC funding", "hire up a team", "talk to
| customers", "find product market fit" is just not fast enough.
|
| Contrast that how quickly Adobe rolled out Generative Fill, a
| product that will keep people subscribed to Photoshop. (e.g. it
| changed my photography practice in that now I can quickly
| remove power lines, draw an extra row of bricks, etc. I don't
| do "AI art" but I now have a buddy that helps retouch photos
| while keeping it real)
|
| If they went and screwed around with some startup they'd add
| six months to a project like that unless it was absolutely in
| the place where they needed to be.
|
| (2) If you were like Pinecone and working on this stuff before
| it was cool you might be a somebody but if you just got into
| A.I. because it was hot, or if you pivoted from "blockchain" or
| if you've ever said both of those things in one sentence I am
| sorry but you are a nobody, you are somebody behind the curve
| not ahead of the curve.
|
| (3) I've worked for startups and done business development in
| this area years before it was cool and I can say it is tough.
| lolinder wrote:
| #1 is a really interesting point. Traditionally startups have
| a velocity advantage over the big companies because they
| don't have all the red tape, but in AI the big companies seem
| to have the advantage. The amount of data you need for
| training and the compute resources required means that
| startups are stuck with APIs that someone else provides, but
| a giant company like Adobe can train their own very quickly
| just based on the research papers that are out there and
| their own data.
| PaulHoule wrote:
| Some of it is that a VC-based company that just got funding
| today is not going to have any product _at all_ for at
| least six months or a year if not longer.
|
| Somebody who needs a system built for their business right
| now gains very little talking to them.
|
| If a startup is a year or two post funding it might really
| have something to offer, but the huge crop of A.I. startups
| funded in the last six months have missed the bus.
|
| Big co's can frequently move very fast when there is a lot
| on the line.
| paxys wrote:
| This isn't unique to AI. If you are hesitant to invest in
| startups because their products could be duplicated by
| competitors/big tech then you should not be a VC at all.
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