I believe that Google Memo answered this entire line of questioning very well:

https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...

"We Have No Moat, And Neither Does *"

The problem with building AI products is that as long as you don't know why or how it works, your competition can just imitate the surface-visible results and it's as good as your product because they also have no clue about why or how it works, just like you.

Maybe I haven’t drank enough coffee today, but what exactly is the takeaway here?

Unless I’m missing something this article is basically saying a failed startup that couldn’t find product market fit was acquired by a large company for its team and some of its tech.

Maybe I skimmed the article too quick, but this exact movie is one that has been playing for decades.

Edit: Side note, my personal opinion on AI is that companies with existing distribution and existing audiences will be the ones that succeed (e.g. Notion layering AI on top its widely used existing wiki platform). Succeeding by building pure tech with no pre-established audience will be very hard.

If only there were something that might help us understand what we were getting into.

There really isn't a moat with these so called 'AI startups' especially those with VC money and are still unprofitable whilst pumping their valuation by overusing AI buzzwords pretending to challenge Google.

Neeva was a solution in search of a problem and almost no one cared to pay to search for results worse than Google. Their situation was so expensive that it wasn't enough for Neeva to make any money to break even and cover their compute costs.

This is the entire race to zero, where Stability, Apple, Meta are already at the finish line with other open source AI models or on-device inference with consumer hardware already available. O̶p̶e̶n̶AI.com and other hosted AI services cannot compete against open source models or freely available models and that is why O̶p̶e̶n̶AI.com needed to cry to regulators to introduce AI licensing rules that benefit them over actual open source or freely available AI models; i.e regulatory capture.

I can see many of these lesser known 'AI startups' getting acquired or shutdown and the bigger companies in AI actually doing AI research still being around much longer. The big money in AI is unsurprisingly hardware and not the software. [0]

[0] https://news.ycombinator.com/item?id=35581777

tl;dr: nothing we didn't know. Since the beginning of times, startups with lots of funding have failed for a number of reasons. AI is no different in that regard.

Somewhere down the line there will also be a very heavy crash when everyone becomes disillusioned with this hype-driven self-proclaimed "AI" revolution (if we can call it that..). Misleading the investors and regulators and manipulating the market. Silicon Valley being SV once again!

My question is whether what the AI startups are selling is fundamentally snake oil or not. Essentially they are all in some way pitching a story of applying what people see in ChatGPT in some way, but usually based on some in-house LLM, custom training or tuning etc, nearly always at vastly smaller scale.

Which all begs the question, how much of what people are seeing is result of the sheer incredible model sizes in ChatGPT - 170b parameters for 3.5 and people are saying it could be 100 trillion in ChatGPT4. If network sizes of that scale are fundamental to achieving the kind of results people are expecting then all of these startups are going to fail.

The most interesting discussion in the post to me is about nVidia. I am curious why, or how long, it will take for what happened with crypto to happen with Transformers - that is, why don't we have custom training hardware yet? Are we just too early? Or is it because the operation is fundamentally so memory intensive that the economics are totally different to crypto, where it's all about the computation and not about storing massive amounts of state?

If it's true that the fundamentals of this are such that custom silicon can't help, nVidia looks to be a huge winner. Who knows which of these AI startups are going to win, but they are all going to buy GPUs from nVidia (or rent them from the cloud). nVidia is going to be the arms dealer in the coming war.

> But I do, however, know of a very large number of standing open offers among tech executives that are more than ready to try out a different kind of hardware than what Nvidia offers if a truly competitive one ever actually materializes.

I’ve read this sentence four times and I can’t figure out what it is saying. Is it missing a word or something, or am I missing a piece of my brain?

More generally, I guess I’m not the target audience for the upcoming paid version of this newsletter, because I can’t extract much meaning or particular insight from anything I’ve read here. Though it did make me feel a little foolish for not tossing a little money toward NVDA, say, six months ago when it should have been pretty darn obvious to me that they’re the ones selling the shovels in this particular gold rush.

Neeva jumped on the AI train after GPT3. The original plan was a privacy-focused Search. People don't pay for that stuff. I can't believe $millions was invested in that idea.

Personalized search - a search engine for all your content based on the data you've collected and visited and private would be something people might pay for.

Where we're going we don't need acquisitions.

Neeva was TBTF. Had nothing to do with AI.

This is the first I’m hearing about Neeva shuttering it’s search product and I’m a paid subscriber………

Ironically the Neeva AI features were the thing that made me stop using it after I’d been using it for about 3 months.

Oh well, further proof you can trust businesses to be anything except businesses if they haven’t proven themselves yet. Promises are empty until delivered.

After seeing the news of Neeva shutting down, it was interesting to listen to Sridhar Ramaswamy on the No Priors podcast just ~1 month prior, talking about how the company had found product market fit and their "Aha!" moment, etc, and just generally presenting the kind of incredibly positive image one expects a founder to relentlessly present. But I guess in reality things were extremely different. Always be skeptical when someone is "talking their book." They will always say things are great, regardless of the situation, and sometimes it's actually a clue that things are even worse, as that's why they're on a media tour instead of working. But hey, $150 million isn't the worst way to fail.

Its quite likely that the AI hype (or more precisely the LLM hype) will deflate like the crypto hype even if for a different set of reasons.

People have been conditioned on expecting the next big thing, FOMO on trillion dollar companies, winner-takes-all etc. as if that is the new normal.

Its quite unlikely that things will play out this way again. The iphone moment or the google moment or the facebook moment will not keep giving "tech" moments.

Tech has both arrived and also bitten more than it can chew. Take for example the rise and fall of crypto. It may have degenerated into inane speculation but not before raising fear deep in the core of the financial system. This is was shutdown libra and prompted cbdc discussions.

Technically we have definetely turned a page in the last decade: algorithms and network protocols keep evolving.

But we have not found a new social equilibrium on how to adopt and deploy new business models. Privacy, sovereignty, power and control, regulation are now center stage.

Tech startups changing the world (for good or bad) does not look like the mode for this decade.

Established entities selling shovels will milk the trend for a while but it feels that the next decade will be decidedly different (mostly regulated corporate adoption rather than startupy).