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.
The reality is also that the "holy shit" moment around LLMs seems to have largely passed. Models are incrementally improving, sure, but the fundamental limitations are here to stay (at least for now). That means a long tail of adding guard rails, etc.
At the end of the day, the value add is also around integration and implementation and that is very difficult to generalize.
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.
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.
There is also the wrong uses of the tech. Some people think you can train a model with any amount of data you may have and that the model will be useful somehow.
There’s a SaaS company in Brazil which has a solution for recruitment that is a text book case of bad usage of machine learning. Not going to mention them here, but their “resume matching by AI” is completely bonkers because of that.
But, enterprise don’t care if it works, it just needs to have this bullet point so the HR director can say he implemented a selection tool powered by AI on her next presentation.
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.
Right now AI produces code that competent coders can massage into the real thing or copy that competent writers can massage into the real thing. I've not seen any evidence it's ever going to turn that corner, but obviously the future is unpredictable.
Curiously, I observed that many investors (or at least people proposing to pitch investors) focus on the simplest of use cases. Perhaps the investor crowd is betting that they can consolidate a few of the successful stories? Or are simply looking to fund many "app" teams to complement their large VC bets in foundation models?
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.
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.
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).
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.
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?"
(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.
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.
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.
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
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.
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.
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".
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.
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.
> 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.
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.
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.
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.
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.
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.
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.
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!
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.
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.
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.
[+] [-] gpvos|2 years ago|reply
[+] [-] codegeek|2 years ago|reply
[+] [-] alfalfasprout|2 years ago|reply
At the end of the day, the value add is also around integration and implementation and that is very difficult to generalize.
[+] [-] bushbaba|2 years ago|reply
[+] [-] ben_w|2 years ago|reply
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.
[+] [-] elzbardico|2 years ago|reply
[+] [-] dehrmann|2 years ago|reply
[+] [-] EA-3167|2 years ago|reply
[+] [-] bandrami|2 years ago|reply
[+] [-] alex1212|2 years ago|reply
[+] [-] Grimburger|2 years ago|reply
Producthunt has basically become that these days, none of it is inspirational nor value adding, just constant "X but with AI"
[+] [-] lumost|2 years ago|reply
[+] [-] strangattractor|2 years ago|reply
[+] [-] mach1ne|2 years ago|reply
[+] [-] soulofmischief|2 years ago|reply
[+] [-] dudeinhawaii|2 years ago|reply
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|2 years ago|reply
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.
[+] [-] tamimio|2 years ago|reply
[+] [-] alex1212|2 years ago|reply
[+] [-] JeremyNT|2 years ago|reply
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).
[+] [-] kdamica|2 years ago|reply
[+] [-] nkohari|2 years ago|reply
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.
[+] [-] spamizbad|2 years ago|reply
[+] [-] PaulHoule|2 years ago|reply
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.
[+] [-] version_five|2 years ago|reply
It's just software, there's little "secret sauce" in the engineering, it's the knowledge of the customer problem that's the differentiator.
[+] [-] claytonjy|2 years ago|reply
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.
[+] [-] yujian|2 years ago|reply
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
[+] [-] chriskanan|2 years ago|reply
If one can scrape the data from the web, I can't imagine having much of a moat or selling point.
[+] [-] paxys|2 years ago|reply
[+] [-] dgb23|2 years ago|reply
Just like forms over SQL, there seems to be a never ending demand.
[+] [-] paulddraper|2 years ago|reply
All the usual things.
First mover
Features
Integrations
Platform synergies
[+] [-] alex1212|2 years ago|reply
[+] [-] staunton|2 years ago|reply
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".
[+] [-] rsynnott|2 years ago|reply
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.
[+] [-] dadoomer|2 years ago|reply
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|2 years ago|reply
Some of the companies involved are now worth trillions.
[+] [-] reilly3000|2 years ago|reply
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|2 years ago|reply
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.
[+] [-] happytiger|2 years ago|reply
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.
[+] [-] jasfi|2 years ago|reply
[+] [-] zby|2 years ago|reply
[+] [-] vasili111|2 years ago|reply
[+] [-] twobitshifter|2 years ago|reply
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.
[+] [-] twelve40|2 years ago|reply
[+] [-] mi3law|2 years ago|reply
[+] [-] janalsncm|2 years ago|reply
[+] [-] badrabbit|2 years ago|reply
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!
[+] [-] phillipcarter|2 years ago|reply
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.
[+] [-] gmerc|2 years ago|reply
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.
[+] [-] rvz|2 years ago|reply
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.