20 ms·

Investors are happy to pay premium for tech, but not for AI

181 points by alex1212 3y ago

gpvos 3y ago
https://archive.ph/qwSAH https://archive.ph/qwSAH
spamizbad 3y ago
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?"
- paxys 3y ago
  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.
- version_five 3y ago
  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 3y ago
    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.)
- PaulHoule 3y ago
  (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 3y ago
    #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 3y ago
      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.
- dgb23 3y ago
  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 3y ago
    Is Jasper a counterexample? Good UX, domain-specific, but still a standalone ChatGPT wrapper forced to do layoffs because they have no moat.
    - alex1212 3y ago
      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.
- alex1212 3y ago
  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 3y ago
  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 3y ago
    Its tricky because by definition the more use-case specific the data the harder to obtain at scale, with some exceptions.
- claytonjy 3y ago
  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.
  - alex1212 3y ago
    Research / talent is how Mistral was able to justify its valuation at 3 weeks old. Pre product, pre anything.
    - claytonjy 3y ago
      Yes, totally. Not something anyone reading this is going to replicate!
      - alex1212 3y ago
        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.
      - Palmik 3y ago
        > 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 3y ago
- rvz 3y ago
  > "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.
  - rvz 3y ago
    So after waiting for more responses falling for that trick question, no-one here has still realized that companies with the hardware are the ones that have the 'moat' which CANNOT be replicated by a dozen competitors. So all these AI startup companies depending on cloud AI services or even open source models have no moat. Only the same big tech incumbents.
- paulddraper 3y ago
  Everything can be replicated with time and money. All the usual things. First mover Features Integrations Platform synergies
- yujian 3y ago
  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
rvz 3y ago
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.
nkohari 3y ago
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.
- coding123 3y ago
  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.
- coffeebeqn 3y ago
  I’m fairly certain some are just prompt prefixes. Maybe a lookup to some 1st or 3rd party dataset
- qaq 3y ago
  So many startups are just a paper-thin layers over publicly-available AWS services.
  - nkohari 3y ago
    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 3y ago
    Some other company's LLM being your secret sauce is different than using AWS services to build your secret sauce on top of.
codegeek 3y ago
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.
- alex1212 3y ago
  Definitely a hype cycle at the moment. I am old enough that this is my second ;)
  - hattmall 3y ago
    Second hype cycle or second AI hype cycle? If the latter when was the first?
    - yxre 3y ago
      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-cycle/ https://www.hiig.de/en/a-brief-history-of-ai-ai-in-the-hype-...
      - padolsey 3y ago
        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
        timy2shoes 3y ago
        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.
jasfi 3y ago
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.
happytiger 3y ago
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.
akokanka 3y ago
Are investors still in cyber security startups or is that train long gone?
- alex1212 3y ago
  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.
tamimio 3y ago
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 3y ago
  No, we all are. Getting super tired by anything in the space
- kdamica 3y ago
  I can’t abide another text-to-SQL pitch
  - lobsterthief 3y ago
    Or another coworker telling me they use it to generate regexes… I get it, but for me writing and testing it is part of the same process so I’m good, thanks.
- JeremyNT 3y ago
  All of these wannabe startups just wrapping ChatGPT or other models and trying to resell them are pretty laughable. Chatbots are going to become "features" in more tangible products, not "products" themselves that people are going to buy (or at least, they sure aren't going to buy them from "value add" resellers).
twobitshifter 3y ago
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.
dadoomer 3y ago
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 3y ago
  Were expectations too high? Some of the companies involved are now worth trillions.
  - robryan 3y ago
    Probably the same with AI, the short term hype cycle being too early for the vast majority of companies.
  - rsynnott 3y ago
    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 3y ago
      I would argue Google because the dot-com bubble did not burst till 2000, and Google was founded in 1998. Also, Netflix was founded Sep 1997. Also, Paypal, founded Oct 1999. Also, Ebay, founded 1995, [del: though Ebay and Paypal together today are worth only 23.8 billion dollars. :del] I think a lot of the money made by founders and angel investors during the dot-com era was made by selling a startup to an established company for 100s of millions of dollars, which is pretty good money since it often took only took 5 years to go from the founding event to such an exit, but I cannot think of an easy way to get a list of such exits. ADDED. In writing the above, I assumed Paypal was still part of Ebay. In reality they are separate companies again with market caps of 23.8 and 84.59 billion dollars.
vasili111 3y ago
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.
zby 3y ago
I have the feeling that we are at the MRP stage (https://en.wikipedia.org/wiki/Material_requirements_planning 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.
rmbyrro 3y ago
http://web.archive.org/web/20230731171759/https://www.bloomberg.com/news/articles/2023-07-31/investors-are-happy-to-pay-premium-for-tech-but-not-for-ai http://web.archive.org/web/20230731171759/https://www.bloomb...
dudeinhawaii 3y ago
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 3y ago
  "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.
twelve40 3y ago
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.
- mi3law 3y ago
  Aye. For someone building in AI, it's been a heart wrenching few months. Very few know what they're talking about.
- janalsncm 3y ago
  Any specific examples you can think of? I am curious.
  - twelve40 3y ago
    > Here are the 9 insane AI websites that will save you hundreds of hours! you can start by googling the above, for example
rsynnott 3y ago
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.
- skirmish 3y ago
  Isn't that the definition of a technological singularity: technology advances at an incomprehensible rate?
  - rsynnott 3y ago
    VCs getting unreasonably excited about a thing for a year or so != technology.
  - archgoon 3y ago
    [dead]
  - tim333 3y ago
    I think the guys behind the idea pictured something a little different. "...Appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue" was apparently what John von Neumann said.
reilly3000 3y ago
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.
- moab 3y ago
  > LLM-enabled senior engineers are 100X more efficient and safe than brand new junior devs. Come on man. Having seen the inside of big-tech-TM and the senior engineers there, yes they are fast and good, but they are not 100x better than the new guy. Maybe 3--5x at best. Anyway how do you train good senior engineers? They don't just pop up out of thin air.
  - morgango 3y ago
    Amen. Even an LLM enabled senior engineer was 100x better, the situation has to be the right one where all 100x can be applied productively. An analogy -- if I am an athlete who runs a mile 100x faster than my competition, does that mean that my country will win 100x more medals at the Olympics? Not unless I compete in every competition. Now, if you can make a system that consistently creates or recruits (or even just prevents your competition from finding) more productive developers you can get a real advantage. And, as soon as you do it you will have people imitating your strategy and tactics and poaching the people who are most important to building the system. It is way more complex than providing an LLM.
  - moonchrome 3y ago
    > Maybe 3--5x at best. More like infinity since juniors usually can't independently progress on non trivial tasks (and will probably regres if left to try). I sort of get what OP is saying, if LLMs get better you can delegate stuff you usually delegate to juniors. But that's nowhere near reality right now, LLMs are not even a net positive with all the failure modes and poor workflow. It takes too much compute to get meaningful context. Having to summarize and chunk problems is more work than the benefits. Copilot is great at speeding up obvious typing, but that's like 10% bump.
gumballindie 3y ago
AI is just another tool in the hands of experienced engineers. Nothing new under the sun.
staunton 3y ago
The thing you need in this space to make money is "owning a platform". It's not clear yet what shapes such platforms are going to take. Right now, the only real condender seems to be owning and API-letting a base model. If OSS models get good, that proposition disappears. Other than that, I haven't seen anything with monopoly potential coming from startups so far. This is what VC is looking for, though: The potential to make a platform to rent out to people who produce the value, curate it and pay for it. Basically, the next "academic publishing".
phillipcarter 3y ago
I'm excited for the metric boatload of incremental value over a long period of time that modern AI is going to deliver for businesses. Because that seems to be the path we're actually on. I suppose there's plenty for investors to love in that equation, but it's a little harder to attach a hype machine to that.
j45 3y ago
It seems a tad bit ironic that the thing that might actually work sooner rather than later (conversational LLM) to have some applications is being shunned.
fatih-erikli 3y ago
Well people would like to pay the things that work – not "may work"
- deleted 3y ago
  [deleted]
- pessimizer 3y ago
  Cryptocurrency would like to have a word with you.
worrycue 3y ago
Well, IMHO high interest rates - meaning no cheap money - also plays a role. AI blew up at a bad time while cryptocurrency lucked out.
_8j50 3y ago
The LLM solutions I am seeing in my space are lackluster and feel like solutions looking for a problem. There are key things LLMs can help with that aren't "revolutionary" or "sexy" but a lot of what I am seeing is saving time on not so tedious things or content generation where I have to carefuly comb through the content for hallucinations or incorrect stuff and risk wild goose chases. The type of solutions I am sorely missing have to do with adding data/work. E.g.: don't tell me what code to use, find me stackoverflow entries that might be highly relevant instead! Don't tell me what the data I am looking at means and have me google that separately, use LLM contextual "undersranding" to find best source material describing what I am looking at or helping me piece together a bigger picture!
gmerc 3y ago
Basically most investors are herd animals. Right now the narrative in SV is that big companies will take all the gains. We are following normal trajectories here - we are in the skeuromorphic phase where we adapt things form one system to another - X, but with AI. Think early iphone - tape recorder , notebook, compass. Those phases tend to benefit agile incumbents with tech companies generally are. Next comes the phase where we make use of the technology in a native way, new products, etc. That’s where you ought to be investing. The second issue is that the user interface is captured by rent seeking incumbents. Many investors want to be that and are disappointed by anything that doesn’t open that opportunity. Crypto had it for them because you’d cut out the bank incumbents. AI is gonna live on platforms we already choose, so not as exciting.
gdedhitchhiker 3y ago
Looks like some companies like Comet are going out of biz so not suprising.
g3ger1ub 3y ago
AI has received considerable media attention and hype, which has lead to both heightened interest and skepticism among investors. Some might be cautious due to concerns about overvaluation or the actual viability of certain AI projects.
Krzyckumw 3y ago
Investors are more willing to pay a premium for established tech companies with proven revenue streams and profitability. On the other hand, AI investments may be viewed with a longer-term horizon, which could impact valuation considerations.

181 points by alex1212 3y ago

https://archive.ph/qwSAH https://archive.ph/qwSAH

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?"

paxys 3y ago
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.
version_five 3y ago
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 3y ago
  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.)
PaulHoule 3y ago
(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 3y ago
  #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 3y ago
    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.
dgb23 3y ago
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 3y ago
  Is Jasper a counterexample? Good UX, domain-specific, but still a standalone ChatGPT wrapper forced to do layoffs because they have no moat.
  - alex1212 3y ago
    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.
alex1212 3y ago
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 3y ago
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 3y ago
  Its tricky because by definition the more use-case specific the data the harder to obtain at scale, with some exceptions.
claytonjy 3y ago
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.
- alex1212 3y ago
  Research / talent is how Mistral was able to justify its valuation at 3 weeks old. Pre product, pre anything.
  - claytonjy 3y ago
    Yes, totally. Not something anyone reading this is going to replicate!
    - alex1212 3y ago
      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.
    - Palmik 3y ago
      > 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 3y ago
rvz 3y ago
> "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.
- rvz 3y ago
  So after waiting for more responses falling for that trick question, no-one here has still realized that companies with the hardware are the ones that have the 'moat' which CANNOT be replicated by a dozen competitors. So all these AI startup companies depending on cloud AI services or even open source models have no moat. Only the same big tech incumbents.
paulddraper 3y ago
Everything can be replicated with time and money. All the usual things. First mover Features Integrations Platform synergies
yujian 3y ago
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

rvz 3y ago

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.

nkohari 3y ago

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.

coding123 3y ago
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.
coffeebeqn 3y ago
I’m fairly certain some are just prompt prefixes. Maybe a lookup to some 1st or 3rd party dataset
qaq 3y ago
So many startups are just a paper-thin layers over publicly-available AWS services.
- nkohari 3y ago
  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 3y ago
  Some other company's LLM being your secret sauce is different than using AWS services to build your secret sauce on top of.

codegeek 3y ago

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.

alex1212 3y ago
Definitely a hype cycle at the moment. I am old enough that this is my second ;)
- hattmall 3y ago
  Second hype cycle or second AI hype cycle? If the latter when was the first?
  - yxre 3y ago
    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-cycle/ https://www.hiig.de/en/a-brief-history-of-ai-ai-in-the-hype-...
    - padolsey 3y ago
      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
      - timy2shoes 3y ago
        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.

jasfi 3y ago

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.

happytiger 3y ago

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.

akokanka 3y ago

Are investors still in cyber security startups or is that train long gone?

alex1212 3y ago
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.

tamimio 3y ago

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 3y ago
No, we all are. Getting super tired by anything in the space
kdamica 3y ago
I can’t abide another text-to-SQL pitch
- lobsterthief 3y ago
  Or another coworker telling me they use it to generate regexes… I get it, but for me writing and testing it is part of the same process so I’m good, thanks.
JeremyNT 3y ago
All of these wannabe startups just wrapping ChatGPT or other models and trying to resell them are pretty laughable. Chatbots are going to become "features" in more tangible products, not "products" themselves that people are going to buy (or at least, they sure aren't going to buy them from "value add" resellers).

twobitshifter 3y ago

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.

dadoomer 3y ago

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 3y ago
Were expectations too high? Some of the companies involved are now worth trillions.
- robryan 3y ago
  Probably the same with AI, the short term hype cycle being too early for the vast majority of companies.
- rsynnott 3y ago
  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 3y ago
    I would argue Google because the dot-com bubble did not burst till 2000, and Google was founded in 1998. Also, Netflix was founded Sep 1997. Also, Paypal, founded Oct 1999. Also, Ebay, founded 1995, [del: though Ebay and Paypal together today are worth only 23.8 billion dollars. :del] I think a lot of the money made by founders and angel investors during the dot-com era was made by selling a startup to an established company for 100s of millions of dollars, which is pretty good money since it often took only took 5 years to go from the founding event to such an exit, but I cannot think of an easy way to get a list of such exits. ADDED. In writing the above, I assumed Paypal was still part of Ebay. In reality they are separate companies again with market caps of 23.8 and 84.59 billion dollars.

vasili111 3y ago

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.

zby 3y ago

I have the feeling that we are at the MRP stage (https://en.wikipedia.org/wiki/Material_requirements_planning 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.

rmbyrro 3y ago

http://web.archive.org/web/20230731171759/https://www.bloomberg.com/news/articles/2023-07-31/investors-are-happy-to-pay-premium-for-tech-but-not-for-ai http://web.archive.org/web/20230731171759/https://www.bloomb...

dudeinhawaii 3y ago

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 3y ago
"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.

twelve40 3y ago

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.

mi3law 3y ago
Aye. For someone building in AI, it's been a heart wrenching few months. Very few know what they're talking about.
janalsncm 3y ago
Any specific examples you can think of? I am curious.
- twelve40 3y ago
  > Here are the 9 insane AI websites that will save you hundreds of hours! you can start by googling the above, for example

rsynnott 3y ago

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.

skirmish 3y ago
Isn't that the definition of a technological singularity: technology advances at an incomprehensible rate?
- rsynnott 3y ago
  VCs getting unreasonably excited about a thing for a year or so != technology.
- archgoon 3y ago
  [dead]
- tim333 3y ago
  I think the guys behind the idea pictured something a little different. "...Appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue" was apparently what John von Neumann said.

reilly3000 3y ago

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.

moab 3y ago
> LLM-enabled senior engineers are 100X more efficient and safe than brand new junior devs. Come on man. Having seen the inside of big-tech-TM and the senior engineers there, yes they are fast and good, but they are not 100x better than the new guy. Maybe 3--5x at best. Anyway how do you train good senior engineers? They don't just pop up out of thin air.
- morgango 3y ago
  Amen. Even an LLM enabled senior engineer was 100x better, the situation has to be the right one where all 100x can be applied productively. An analogy -- if I am an athlete who runs a mile 100x faster than my competition, does that mean that my country will win 100x more medals at the Olympics? Not unless I compete in every competition. Now, if you can make a system that consistently creates or recruits (or even just prevents your competition from finding) more productive developers you can get a real advantage. And, as soon as you do it you will have people imitating your strategy and tactics and poaching the people who are most important to building the system. It is way more complex than providing an LLM.
- moonchrome 3y ago
  > Maybe 3--5x at best. More like infinity since juniors usually can't independently progress on non trivial tasks (and will probably regres if left to try). I sort of get what OP is saying, if LLMs get better you can delegate stuff you usually delegate to juniors. But that's nowhere near reality right now, LLMs are not even a net positive with all the failure modes and poor workflow. It takes too much compute to get meaningful context. Having to summarize and chunk problems is more work than the benefits. Copilot is great at speeding up obvious typing, but that's like 10% bump.

gumballindie 3y ago

AI is just another tool in the hands of experienced engineers. Nothing new under the sun.

staunton 3y ago

The thing you need in this space to make money is "owning a platform". It's not clear yet what shapes such platforms are going to take. Right now, the only real condender seems to be owning and API-letting a base model. If OSS models get good, that proposition disappears. Other than that, I haven't seen anything with monopoly potential coming from startups so far. This is what VC is looking for, though: The potential to make a platform to rent out to people who produce the value, curate it and pay for it. Basically, the next "academic publishing".

phillipcarter 3y ago

I'm excited for the metric boatload of incremental value over a long period of time that modern AI is going to deliver for businesses. Because that seems to be the path we're actually on. I suppose there's plenty for investors to love in that equation, but it's a little harder to attach a hype machine to that.

j45 3y ago

It seems a tad bit ironic that the thing that might actually work sooner rather than later (conversational LLM) to have some applications is being shunned.

fatih-erikli 3y ago

Well people would like to pay the things that work – not "may work"

deleted 3y ago
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pessimizer 3y ago
Cryptocurrency would like to have a word with you.

worrycue 3y ago

Well, IMHO high interest rates - meaning no cheap money - also plays a role. AI blew up at a bad time while cryptocurrency lucked out.

_8j50 3y ago

The LLM solutions I am seeing in my space are lackluster and feel like solutions looking for a problem. There are key things LLMs can help with that aren't "revolutionary" or "sexy" but a lot of what I am seeing is saving time on not so tedious things or content generation where I have to carefuly comb through the content for hallucinations or incorrect stuff and risk wild goose chases. The type of solutions I am sorely missing have to do with adding data/work. E.g.: don't tell me what code to use, find me stackoverflow entries that might be highly relevant instead! Don't tell me what the data I am looking at means and have me google that separately, use LLM contextual "undersranding" to find best source material describing what I am looking at or helping me piece together a bigger picture!

gmerc 3y ago

Basically most investors are herd animals. Right now the narrative in SV is that big companies will take all the gains. We are following normal trajectories here - we are in the skeuromorphic phase where we adapt things form one system to another - X, but with AI. Think early iphone - tape recorder , notebook, compass. Those phases tend to benefit agile incumbents with tech companies generally are. Next comes the phase where we make use of the technology in a native way, new products, etc. That’s where you ought to be investing. The second issue is that the user interface is captured by rent seeking incumbents. Many investors want to be that and are disappointed by anything that doesn’t open that opportunity. Crypto had it for them because you’d cut out the bank incumbents. AI is gonna live on platforms we already choose, so not as exciting.

gdedhitchhiker 3y ago

Looks like some companies like Comet are going out of biz so not suprising.

g3ger1ub 3y ago

AI has received considerable media attention and hype, which has lead to both heightened interest and skepticism among investors. Some might be cautious due to concerns about overvaluation or the actual viability of certain AI projects.

Krzyckumw 3y ago

Investors are more willing to pay a premium for established tech companies with proven revenue streams and profitability. On the other hand, AI investments may be viewed with a longer-term horizon, which could impact valuation considerations.